How to Write a Research Paper

Writing a research paper is a bit more difficult that a standard high school essay. You need to site sources, use academic data and show scientific examples. Before beginning, you’ll need guidelines for how to write a research paper.

Before you begin writing the research paper, you must do your research. It is important that you understand the subject matter, formulate the ideas of your paper, create your thesis statement and learn how to speak about your given topic in an authoritative manner. You’ll be looking through online databases, encyclopedias, almanacs, periodicals, books, newspapers, government publications, reports, guides and scholarly resources. Take notes as you discover new information about your given topic. Also keep track of the references you use so you can build your bibliography later and cite your resources.

Develop Your Thesis Statement

When organizing your research paper, the thesis statement is where you explain to your readers what they can expect, present your claims, answer any questions that you were asked or explain your interpretation of the subject matter you’re researching. Therefore, the thesis statement must be strong and easy to understand. Your thesis statement must also be precise. It should answer the question you were assigned, and there should be an opportunity for your position to be opposed or disputed. The body of your manuscript should support your thesis, and it should be more than a generic fact.

Create an Outline

Many professors require outlines during the research paper writing process. You’ll find that they want outlines set up with a title page, abstract, introduction, research paper body and reference section. The title page is typically made up of the student’s name, the name of the college, the name of the class and the date of the paper. The abstract is a summary of the paper. An introduction typically consists of one or two pages and comments on the subject matter of the research paper. In the body of the research paper, you’ll be breaking it down into materials and methods, results and discussions. Your references are in your bibliography. Use a research paper example to help you with your outline if necessary.

Organize Your Notes

When writing your first draft, you’re going to have to work on organizing your notes first. During this process, you’ll be deciding which references you’ll be putting in your bibliography and which will work best as in-text citations. You’ll be working on this more as you develop your working drafts and look at more white paper examples to help guide you through the process.

Write Your Final Draft

After you’ve written a first and second draft and received corrections from your professor, it’s time to write your final copy. By now, you should have seen an example of a research paper layout and know how to put your paper together. You’ll have your title page, abstract, introduction, thesis statement, in-text citations, footnotes and bibliography complete. Be sure to check with your professor to ensure if you’re writing in APA style, or if you’re using another style guide.

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Welcome to TOP 10 research articles

Top 10 data mining papers: recommended reading ? datamining & knowledgement management research, citation count: 85, data mining and its applications for knowledge management: a literature review from 2007 to 2012.

Tipawan Silwattananusarn 1 and KulthidaTuamsuk 2

1 Ph.D. Student in Information Studies Program, Khon Kaen University, Thailand and 2 Head, Information & Communication Management Program, Khon Kaen University, Thailand

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Data mining; Data mining applications; Knowledge management

[1] An, X. & Wang, W. (2010). Knowledge management technologies and applications: A literature review . IEEE, 138-141. doi:10.1109/ICAMS.2010.5553046

[2] Berson, A., Smith, S.J. &Thearling, K. (1999). Building Data Mining Applications for CRM. New York: McGraw-Hill .

[3] Cant�, F.J. & Ceballos, H.G. (2010). A multiagent knowledge and information network approach for managing research assets . Expert Systems with Applications, 37(7), 5272-5284.doi:10.1016/j.eswa.2010.01.012

[4] Cheng, H., Lu, Y. & Sheu, C. (2009). An ontology-based business intelligence application in a financial knowledge management system .Expert Systems with Applications, 36, 3614�3622. Doi:10.1016/j.eswa.2008.02.047

[5] Dalkir, K. (2005). Knowledge Management in Theory and Practice . Boston: Butterworth-Heinemann.

[6] Dawei, J. (2011). The Application of Date Mining in Knowledge Management .2011 International Conference on Management of e-Commerce and e-Government, IEEE Computer Society, 7-9. doi:10.1109/ICMeCG.2011.58

[7] Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases.AI Magazine, 17(3), 37-54.

[8] Gorunescu, F. (2011). Data Mining: Concepts, Models, and Techniques . India: Springer.

[9] Han, J. &Kamber, M. (2012). Data Mining: Concepts and Techniques . 3rd.ed. Boston: Morgan Kaufmann Publishers.

[10] Hwang, H.G., Chang, I.C., Chen, F.J. & Wu, S.Y. (2008). Investigation of the application of KMS for diseases classifications: A study in a Taiwanese hospital . Expert Systems with Applications, 34(1), 725-733. doi:10.1016/j.eswa.2006.10.018

[11] Lavrac, N., Bohanec, M., Pur, A., Cestnik, B., Debeljak, M. &Kobler, A. (2007).Data mining and visualization for decision support and modeling of public health-care resources.Journal of Biomedical Informatics, 40, 438-447. doi:10.1016/j.jbi.2006.10.003

[12] Li, X., Zhu, Z. & Pan, X. (2010). Knowledge cultivating for intelligent decision making in small & middle businesses .Procedia Computer Science, 1(1), 2479-2488. doi:10.1016/j.procs.2010.04.280

[13] Li, Y., Kramer, M.R., Beulens, A.J.M., Van Der Vorst, J.G.A.J. (2010). A framework for early warning and proactive control systems in food supply chain networks. Computers in Industry, 61, 852�862. Doi:101.016/j.compind.2010.07.010

[14] Liao, S.H., Chen, C.M., Wu, C.H. (2008). Mining customer knowledge for product line and brand extension in retailing. Expert Systems with Applications, 34(3), 1763-1776. doi:10.1016/j.eswa.2007.01.036

[15] Liao, S. (2003). Knowledge management technologies and applications-literature review from 1995 to 2002 . Expert Systems with Applications, 25, 155-164. doi:10.1016/S0957-4174(03)00043-5

[16] Liu, D.R. & Lai, C.H. (2011). Mining group-based knowledge flows for sharing task knowledge. Decision Support Systems ,50(2), 370-386. doi:10.1016/j.dss.2010.09.004

[17] Lee, M.R. & Chen, T.T. (2011). Revealing research themes and trends in knowledge management: From 1995 to 2010. Knowledge-Based Systems.doi:10.1016/j.knosys.2011.11.016

[18] McInerney, C.R. & Koenig, M.E. (2011). Knowledge Management (KM) Processes in Organizations: Theoretical Foundations and Practice . USA: Morgan & Claypool Publishers. doi:10.2200/S00323ED1V01Y201012ICR018

[19] McInerney, C. (2002). Knowledge Management and the Dynamic Nature of Knowledge .Journal of the American Society for Information Science and Technology, 53(12), 1009-1018. doi:10.1002/asi.10109

[20] Ngai, E., Xiu, L. &Chau, D. (2009). Application of data mining techniques in customer relationship management: A literature review and classification . Expert Systems with Applications, 36, 2592- 2602. doi:10.1016/j.eswa.2008.02.021

[21] Ruggles, R.L. (ed.). (1997). Knowledge Management Tools. Boston: Butterworth-Heinemann.

[22] Sher, P.J. & Lee, V.C. (2004). Information technology as a facilitator for enhancing dynamic capabilities through knowledge management.Information & Management, 41, 933-945. doi:10.1016/

[23] Tseng, S.M. (2008). The effects of information technology on knowledge management systems .Expert Systems with Applications, 35, 150-160. doi:10.1016/j.eswa.2007.06.011

[24] Ur-Rahman, N. & Harding, J.A. (2012). Textual data mining for industrial knowledge management and text classification: A business oriented approach . Expert Systems with Applications, 39, 4729-4739. doi:10.1016/j.eswa.2011.09.124

[25] Wang, F. & Fan, H. (2008). Investigation on Technology Systems for Knowledge Management.IEEE, 1-4. doi:10.1109/WiCom.2008.2716

[26] Wang, H. & Wang, S. (2008). A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems, 108(5), 622-634.

[27] Wu, W., Lee, Y.T., Tseng, M.L. & Chiang, Y.H. (2010). Data mining for exploring hidden patterns between KM and its performance.Knowledge-Based Systems, 23, 397-401. doi:10.1016/j.knosys.2010.01.014

Citation Count: 83

Analysis of heart diseases dataset using neural network approach.

K. Usha Rani

Dept. of Computer Science, Sri Padmavathi Mahila Visvavidyalayam (Women�s University), Tirupati – 517502 , Andhra Pradesh, India

One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.

Data mining, Classification, Neural Networks, Parallelism, Heart Disease

[1] John Shafer, Rakesh Agarwal, and Manish Mehta, (1996) � SPRINT:A scalable parallel classifier for data mining �, In Proc. Of the VLDB Conference, Bombay, India..

[2] Sunghwan Sohn and Cihan H. Dagli, (2004) � Ensemble of Evolving Neural Networks in classification �, Neural Processing Letters 19: 191-203, Kulwer Publishers.

[3] K. Anil Jain, Jianchang Mao and K.M. Mohiuddi, (1996) � Artificial Neural Networks: A Tutorial �, IEEE Computers, pp.31-44.

[4] George Cybenk,, (1996)�Neural Networks in Computational Science and Engineering�, IEEE Computational Science and Engineering, pp.36-42

[5] R. Rojas, (1996) �Neural Networks: a systematic introduction�, Springer-Verlag.

[6] R.P.Lippmann,�Pattern classification using neural networks, (1989)� IEEE Commun. Mag., pp.47�64.

[7] Simon Haykin, (2001) � Neural Networks � A Comprehensive Foundation �, Pearson Education.

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[9] W. G. Baxt, (1990) � Use of an artificial neural network for data analysis in clinical decisionmaking: The diagnosis of acute coronary occlusion ,� Neural Comput., vol. 2, pp. 480�489..

[10] Dr. A. Kandaswamy, (1997) �Applications of Artificial Neural Networks in Bio Medical Engineering�, The Institute of Electronics and Telecommunicatio Engineers, Proceedings of the Zonal Seminar on Neural Networks, Nov 20-21.

[11] A. Kusiak, K.H. Kernstine, J.A. Kern, K A. McLaughlin and T.L. Tseng, (2000) � Data mining: Medical and Engineering Case Studies �, Proceedings of the Industrial Engineering Research Conference, Cleveland, Ohio, May21-23,pp.1-7.

[12] H. B. Burke, (1994) � Artificial neural networks for cancer research: Outcome prediction ,� Sem. Surg. Oncol., vol. 10, pp. 73�79.

[13] H. B. Burke, P. H. Goodman, D. B. Rosen, D. E. Henson, J. N. Weinstein, F. E. Harrell, J. R.Marks, D. P. Winchester, and D. G. Bostwick, (1997) � Artificial neural networks improve the accuracy of cancer survival prediction ,� Cancer, vol. 79, pp. 857�8621997.

[14] Siri Krishan Wasan1,Vasudha Bhatnagar2 and Harleen Kaur, (2006)� The impact of Data Mining Techniques on Medical Diagnostics�, Data Science Journal, Volume 5, 119-126.

[15] Scales, R., & Embrechts, M., (2002) �Computational Intelligence Techniques for Medical Diagnostic�, Proceedings of Walter Lincoln Hawkins, Graduate Research Conference from the World Wide Web:

[16] S. M. Kamruzzaman , Md. Monirul Islam, (2006)� An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems�, International Journal of Information Technology, Vol. 12 No. 8.

[17] Hasan Temurtas, Nejat Yumusak, Feyzullah Temurtas, (2009)� A comparative study on diabetes disease diagnosis using neural networks�, Expert Systems with Applications: An International Journal , Volume 36 Issue 4. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5, September 2011 8

[18] D Gil, M Johnsson, JM Garcia Chamizo, (2009) , � Application of artificial neural networks in the diagnosis of urological dysfunctions �, Expert Systems with Applications Volume 36, Issue 3, Part 2, Pages 5754-5760, Elsevier

[19] R. Dybowski and V. Gant, (2007), � Clinical Applications of Artificial Neural Networks �, Cambridge University Press.

[20] O. Er, N. Yumusak and F. Temurtas, (2010) “Chest disease diagnosis using artificial neural networks”, Expert Systems with Applications, Vol.37, No.12, pp. 7648-7655.

[21] S. Moein, S. A. Monadjemi and P. Moallem, (2009) “ A Novel Fuzzy-Neural Based Medical Diagnosis System “, International Journal of Biological & Medical Sciences, Vol.4, No.3, pp. 146-150.

Citation Count: 80

Predicting students? performance using id3 and c4.5 classification algorithms.

Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao

Department of Computer Engineering, Fr. C.R.I.T., Navi Mumbai, Maharashtra, India

An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.

Classification, C4.5, Data Mining, Educational Research, ID3, Predicting Performance

[1] Han, J. and Kamber, M., (2006) Data Mining: Concepts and Techniques , Elsevier.

[2] Dunham, M.H., (2003) Data Mining: Introductory and Advanced Topics, Pearson Education Inc.

[3] Kantardzic, M., (2011) Data Mining: Concepts, Models, Methods and Algorithms, Wiley-IEEE Press.

[4] Ming, H., Wenying, N. and Xu, L., (2009) �An improved decision tree classification algorithm based on ID3 and the application in score analysis�, Chinese Control and Decision Conference (CCDC), pp1876-1879.

[5] Xiaoliang, Z., Jian, W., Hongcan Y., and Shangzhuo, W., (2009) � Research and Application of the improved Algorithm C4.5 on Decision Tree �, International Conference on Test and Measurement (ICTM), Vol. 2, pp184-187.

[6] CodeIgnitor User Guide Version 2.14,

[7] RapidMiner,

[8] MySQL � The world�s most popular open source database,

Citation Count: 51

Diagnosis of diabetes using classification mining techniques.

Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly

Department of Computer Science, BITS Pilani Dubai, United Arab Emirates

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its ontinued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Na�ve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

Classification, Data Mining, Decision Tree, Diabetes and Na�ve Bayes.

[1] National Diabetes Information Clearinghouse (NDIC),

[2] Global Diabetes Community,

[3] Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques�, Morgan Kauffman Publishers, 2001

[4] S. Kumari and A. Singh, � A Data Mining Approach for the Diagnosis of Diabetes Mellitus �, Proceedings of Seventh lnternational Conference on Intelligent Systems and Control, 2013, pp. 373-375

[5] C. M. Velu and K. R. Kashwan, �Visual Data Mining Techniques for Classification of Diabetic Patients�, 3rd IEEE International Advance Computing Conference (IACC), 2013

[6] Sankaranarayanan.S and Dr Pramananda Perumal.T, � Predictive Approach for Diabetes Mellitus Disease through Data Mining Technologies �, World Congress on Computing and Communication Technologies, 2014, pp. 231-233

[7] Mostafa Fathi Ganji and Mohammad Saniee Abadeh, �Using fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease�, Proceedings of ICEE 2010, May 11-13, 2010

[8] T.Jayalakshmi and Dr.A.Santhakumaran, � A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks �, International Conference on Data Storage and Data Engineering, 2010, pp. 159-163

[9] Sonu Kumari and Archana Singh, �A Data Mining Approach for the Diagnosis of Diabetes Mellitus�, Proceedings of71hlnternational Conference on Intelligent Systems and Control (ISCO 2013)

[10] Neeraj Bhargava, Girja Sharma, Ritu Bhargava and Manish Mathuria, Decision Tree Analysis on J48 Algorithm for Data Mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.

[11] Michael Feld, Dr. Michael Kipp, Dr. Alassane Ndiaye and Dr. Dominik Heckmann �Weka: Practical machine learning tools and techniques with Java implementations�

[12] White, A.P., Liu, W.Z.: Technical note: Bias in information-based measures in decision tree induction . Machine Learning 15(3), 321�329 (1994)

Citation Count: 42

A new clutering approach for anomaly intrusion detection.

Ravi Ranjan and G. Sahoo

Department of Information Technology, Birla Institute of Technology, Mesra, Ranchi

Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion detection by using the approach of K-medoids method of clustering and its certain modifications. The proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means algorithm.

Clustering, data mining, intrusion detection, network security

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[2] Dorothy E. Denning, �An intrusion-detection model�, IEEE Transactions on software engineering, pp. 222�232, 1987.

[3] Kemmerer, R., and Vigna, G. �Intrusion Detection: A Brief History and Overview.� IEEE Security & Privacy, v1 n1, Apr 2002, p27-30.

[4] S. Staniford-Chen, S. Cheung, R. Crawford., M. Dilger, J. Frank, J. Hoagland, K. Levitt, C.Wee, R.Yip, D. Zerkle . � GrIDS- A Graph-Based Intrusion Detection system for Large Networks .� Proc National Information Systems Security conf, 1996.

[5] M.Jianliang, S.Haikun and B.Ling. The Application on Intrusion Detection based on K- Means Cluster Algorithm . International Forum on Information Technology and Application, 2009.

[6] Yu Guan, Ali A. Ghorbani and Nabil Belacel. Y-means: a clustering method for Intrusion Detection. In Canadian Conference on Electrical and Computer Engineering, pages 14, Montral, Qubec, Canada, May 2003.

[7] Zhou Mingqiang, HuangHui, WangQian, � A Graph-based Clustering Algorithm for Anomaly Intrusion Detection � In computer science and education (ICCSE), 7th International Conference ,2012.

[8] Chitrakar, R. and Huang Chuanhe, � Anomaly detection using Support Vector Machine Classification with K-Medoids clustering � In Internet (AH-ICI), 3rd Asian Himalayas International conference, 2012.

[9] Yang Jian, �An Improved Intrusion Detection Algorithm Based on DBSCAN�, Micro Computer Information, 25,1008-0570(2009)01- 3- 0058-03, 58-60,2009.

[10] Li Xue-yong, Gao Guo- �A New Intrusion Detection Method Based on Improved DBSCAN �, In Information Engineering (ICIE), WASE International conference, 2010.

[11] Lei Li, De-Zhang, Fang-Cheng Shen, � A novel rule-based Intrusion Detection System using data mining �, In ICCSIT, IEEE International conference, 2010.

[12] Z. Muda, W. Yassin, M.N. Sulaiman and N.I.Udzir, � Intrusion Detection based on K-Means Clustering and OneR Classification � In Information Assurance and Security (IAS), 7th International conference, 2011.

[13] Zhengjie Li, Yongzhong Li, Lei Xu, � Anomaly intrusion detection method based on K-means clustering algorithm with particle swarm optimization �, In ICM, 2011.

[14] Kapil Wankhade, Sadia Patka, Ravindra Thool, � An Overview of Intrusion Detection Based on Data Mining Techniques �, In Proceedings of 2013 International Conference on Communication Systems and Network Technologies, IEEE, 2013, pp.626-629. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.4, No.2, March 2014 38

[15] H. Fatma, L. Mohamed, �A two-stage technique to improve intrusion detection systems based on data mining algorithms�, In ICMSAO, 2013.

[16] A.M. Chandrasekhar, K. Raghuveer, � Intrusion detection technique by using K-means,fuzzy neural network and SVM classifiers �, In ICCCI, 2013.

[17] Margaret H. Dunham, � Data Mining: Introductory and Advanced Topics �,ISBN: 0130888923, published by Pearson Education, Inc.,2003.

[18] KDD.KDDCup1999Data. , 1999.

Citation Count: 34

Incremental learning: areas and methods ?a survey.

Prachi Joshi 1 and Parag Kulkarni 2

1 Assistant Professor, MIT College of Engineering, Pune and 2 Adjunct Professor, College of Engineering, Pune

While the areas of applications in data mining are growing substantially, it has become extremely necessary for incremental learning methods to move a step ahead. The tremendous growth of unlabeled data has made incremental learning take up a big leap. Starting from BI applications to image classifications, from analysis to predictions, every domain needs to learn and update. Incremental learning allows to explore new areas at the same time performs knowledge amassing. In this paper we discuss the areas and methods of incremental learning currently taking place and highlight its potentials in aspect of decision making. The paper essentially gives an overview of the current research that will provide a background for the students and research scholars about the topic.

Incremental, learning, mining, supervised, unsupervised, decision-making

[1] Y. Lui, J. Cai, J. Yin, A. Fu, Clustering text data streams , Journal of Computer Science and Technology, 2008, pp 112-128.

[2] A. Fahim, G. Saake, A. Salem, F. Torky, M. Ramadan, K-means for spherical clusters with large variance in sizes , Journal of World Academy of Science, Engineering and Technology, 2008.

[3] F. Camastra, A. Verri, A novel kernel method for clustering, IEEE Transactions on Pattern Analysis and Machince Intelligence, Vol. 27, no.5, 2005, pp 801-805.

[4] F. Shen, H. Yu, Y. Kamiya, O. Hasegawa, An Online Incremental Semi-Supervised Learning Method , Journal of advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No.6, 2010.

[5] T. Zhang, R. Ramakrishnan, M. Livny, Birch: An efficient data clustering method for very large databases, Proc. ACM SIGMOD Intl.Conference on Management of Data , 1996, pp.103-114.

[6] S. Deelers, S. Auwantanamongkol, Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with highest variance , International Journal of Electrical and Computer Science, 2007, pp 247-252.

[7] S. Young, A. Arel, T. Karnowski, D. Rose, A Fast and Stable Incremental Clustering Algorithm , Proc. of International Conference on Information Technology New Generations, 2010, pp 204-209.

[8] M. Charikar, C. Chekuri, T. Feder, R. Motwani, Incremental clustering and dynamic information retrival, Proc. of ACM symposium on Theory of Computeion , 1997, pp 626- 635.

[9] K. Hammouda, Incremental document clustering using Cluster similarity histograms , Proc. of IEEE International Conference on Web Intelligence, 2003, pp 597- 601.

[10] X. Su, Y. Lan,R. Wan, Y. Qin, A fast incremental clustering algorithm , Proc. of International Symposium on Information Processing, 2009, pp 175-178.

[11] T. Li, HIREL: An incremental clustering for relational data sets , Proc. of IEEE International Conference on Data Mining, 2008, pp 887 � 892.

[12] P. Lin, Z. Lin, B. Kuang, P. Huang, A Short Chinese Text Incremental Clustering Algorithm Based on Weighted Semantics and Naive Bayes , Journal of Computational Information Systems, 2012, pp 4257- 4268.

[13] C. Chen, S. Hwang, Y. Oyang, An Incremental hierarchical data clustering method based on gravity theory , Proc. of PAKDD, 2002, pp 237-250.

[14] M. Ester, H. Kriegel, J. Sander, M. Wimmer, X. Xu, Incremental Clustering for Mining in a Data Warehousing Environment , Proc. of Intl. Conference on very large data bases, 1998, pp 323-333.

[15] G. Shaw, Y. Xu, Enhancing an incremental clustering algorithm for web page collections, Proc. of IEEE/ACM/WIC Joint Conference on Web Intelligence and and Intelligent Agent Technology, 2009.

[16] C. Hsu, Y. Huang, Incremental clustering of mixed data based on distance hierarchy , Journal of Expert systems and Applications, 35, 2008, pp 1177 � 1185.

[17] S. Asharaf, M. Murty, S. Shevade, Rough set based incremental clustering of interval data, Pattern Recognition Letters, Vol.27 (9), 2006, pp 515-519.

[18] Z. Li, Incremental Clustering of trajectories , Computer and Information Science, Springer 2010, pp 32-46.

[19] S. Elnekava, M. Last, O. Maimon, I ncremental clustering of mobile objects , Proc. of IEEE International Conference on Data Engineering, 2007, pp 585-592.

[20] S. Furao, A. Sudo, O. Hasegawa, An online incremental learning pattern -based reasoning system, Journal of Neural Networks, Elsevier, Vol. 23,(1), 2010.pp 135-143.

[21] S. Ferilli, M. Biba, T.Basile, F. Esposito, Incremental Machine learning techniques for document layout understanding , Proc. of IEEE Conference on Pattern Recognition, 2008, pp 1-4.

[22] S. Ozawa, S. Pang, N. Kasabov, Incremental Learning of chunk data for online pattern classification systems, IEEE Transactions on Neural Networks, Vo. 19 (6), 2008, pp 1061-1074.

[23] Z. Chen, L. Huang, Y. Murphey, Incremental learning for text document classification , Proc. of IEEE Conference on Neural Networks, 2007, pp 2592-2597. 51

[24] R. Polikar, L. Upda, S. Upda, V. Honavar, Learn ++: An incremental learning algorithm for supervised neural networks , IEEE Transactions on Systems, Man and Cybernatics, Vol.31 (4), 2001, pp 497-508.

[25] H. He, S. Chen, K. Li, X. Xu, Incremental learning from stream data, IEEE Transactions on Neural Networks , Vol.22(12), 2011, pp 1901-1914.

[26] A. Bouchachia, M. Prosseger, H. Duman, Semi supervised incremental learning, Proc. of IEEE International Conference on Fuzzy Systems, 2010 pp 1-7.

[27] R. Zhang, A. Rudnicky, A new data section principle for semi-supervised incremental learning , Computer Science department, paper 1374, 2006, .

[28] Z. Li, S. Watchsmuch, J. Fritsch, G. Sagerer, Semi-supervised incremental learning of manipulative tasks, Proc. of International Conference on Machine Vision Applications, 2007, pp 73-77.

[29] A. Misra, A. Sowmya, P. Compton, Incremental learning for segmentation in medical images , Proc. of IEEE Conference on Biomedical Imaging, 2006.

[30] P. Kranen, E. Muller, I. Assent, R. Krieder, T. Seidl, Incremental Learning of Medical Data for MultiStep Patient Health Classification, Database technology for life sciences and medicine, 2010.

[31] J. Wu, B. Zhang, X. Hua, J, Zhang, A semi-supervised incremental learning framework for sports video view classification, Proc. of IEEE Conference on Multi-Media Modelling, 2006.

[32] S. Wenzel, W. Forstner, Semi supervised incremental learning of hierarchical appearance models , The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol.37,2008.

[33] S. Ozawa, S. Toh, S. Abe, S. Pang, N. Kasabov, Incremental Learning for online face recognition , Proc. of IEEE Conference on Neural Networks, Vol. 5, 2005 pp 3174-3179.

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Citation Count: 33

A prototype decision support system for optimizing the effectiveness of elearning in educational institutions.

S. Abu-Naser, A. Al-Masri, Y. Abu Sultan and I. Zaqout

Al Azhar University Gaza, Palestine,

In this paper, a prototype of a Decision Support System (DSS) is proposed for providing the knowledge for optimizing the newly adopted e-learning education strategy in educational institutions. If an educational institution adopted e-learning as a new strategy, it should undertake a preliminary evaluation to determine the percentage of success and areas of weakness of this strategy. If this evaluation is done manually, it would not be an easy task to do and would not provide knowledge about all pitfall symptoms. The proposed DSS is based on exploration (mining) of knowledge from large amounts of data yielded from the operating the institution to its business. This knowledge can be used to guide and optimize any new business strategy implemented by the institution. The proposed DSS involves Database engine, Data Mining engine and Artificial Intelligence engine. All these engines work together in order to extract the knowledge necessary to improve the effectiveness of any strategy, including e-learning

DSS, E-learning, knowledge, Database, Data mining, Artificial Intelligence.

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[21]Marta Zorrilla, Diego Garc�a and Elena �lvarez.(2010). A Decision Support System to improve eLearning Environments. BEWEB 2010 – International Workshop on Business intelligence and the WEB ,March 22-26, 2010 – Lausanne (Switzerland).

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Citation Count: 27

Experimental study of data clustering using k-means and modified algorithms.

M.P.S Bhatia and Deepika Khurana

University of Delhi, New Delhi, India

The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in exploratory data analysis. This paper presents results of the experimental study of different approaches to k- Means clustering, thereby comparing results on different datasets using Original k-Means and other modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and execution time.

Data Mining, Clustering Algorithm, k- Means, Silhouette Validity Index.

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[5] Barile� Barisi Baridam,� More work on k-means Clustering algortithm: The Dimensionality Problem �. International Journal of Computer Applications (0975 � 8887)Volume 44� No.2, April 2012.

[6] Shi Na, Li Xumin, Guan Yong �Research on K-means clustering algorithm�. Proc of Third International symposium on Intelligent Information Technology and Security Informatics, IEEE 2010.

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[8] Kohei Arai,Ali Ridho Barakbah, Hierarchical K-means: an algorithm for centroids initialization for K-means.

[9] Data Mining Concepts and Techniques,Second edition Jiawei Han and Micheline Kamber.

[10] �Towards more accurate clustering method by using dynamic time warping� International Journal of Data Mining and Knowledge Management Process (IJDKP), Vol.3, No.2,March 2013.

[11] C. S. Li, � Cluster Center Initialization Method for K-means Algorithm Over Data Sets with Two Clusters �, �2011 International Conference on Advances in Engineering, Elsevier�, pp. 324-328, vol.24, 2011.

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[16] Mu-Chun Su and Chien-Hsing Chou � A Modified version of k-means Algorithm with a Distance Based on Cluster Symmetry �.IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol 23 No. 6 ,June 2001.

Citation Count: 26

Data, text and web mining for business intelligence : a survey.

Abdul-Aziz Rashid Al-Azmi

Department of Computer Engineering, Kuwait University, Kuwait

The Information and Communication Technologies revolution brought a digital world with huge amounts of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden knowledge in large databases or the Internet. Mining tools are automated software tools used to achieve business intelligence by finding hidden relations, and predicting future events from vast amounts of data. This uncovered knowledge helps in gaining completive advantages, better customers� relationships, and even fraud detection. In this survey, we�ll describe how these techniques work, how they are implemented. Furthermore, we shall discuss how business intelligence is achieved using these mining tools. Then look into some case studies of success stories using mining tools. Finally, we shall demonstrate some of the main challenges to the mining technologies that limit their potential

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Citation Count: 21

Applications of data mining techniques in life insurance.

A. B. Devale 1 and R. V. Kulkarni 2

1 Arts, Commerce, Science College, Palus Dist. Sangli, Maharashtra and 2 Shahu Institute of Business Research, Kolhapur, Maharashtra

Knowledge discovery in financial organization have been built and operated mainly to support decision making using knowledge as strategic factor. In this paper, we investigate the use of various data mining techniques for knowledge discovery in insurance business. Existing software are inefficient in showing such data characteristics. We introduce different exhibits for discovering knowledge in the form of association rules, clustering, classification and correlation suitable for data characteristics. Proposed data mining techniques, the decision- maker can define the expansion of insurance activities to empower the different forces in existing life insurance sector.

Insurance, Association rules, Clustering, Classification, Correlation, Data mining.

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50 selected papers in Data Mining and Machine Learning

Here is the list of 50 selected papers in Data Mining and Machine Learning . You can download them for your detailed reading and research. Enjoy!

Data Mining and Statistics: What’s the Connection?

Data Mining: Statistics and More? , D. Hand, American Statistician, 52(2):112-118.

Data Mining , G. Weiss and B. Davison, in Handbook of Technology Management, John Wiley and Sons, expected 2010.

From Data Mining to Knowledge Discovery in Databases , U. Fayyad, G. Piatesky-Shapiro & P. Smyth, AI Magazine, 17(3):37-54, Fall 1996.

Mining Business Databases , Communications of the ACM, 39(11): 42-48.

10 Challenging Problems in Data Mining Research , Q. Yiang and X. Wu, International Journal of Information Technology & Decision Making, Vol. 5, No. 4, 2006, 597-604.

The Long Tail , by Anderson, C., Wired magazine.

AOL’s Disturbing Glimpse Into Users’ Lives , by McCullagh, D.,, August 9, 2006

General Data Mining Methods and Algorithms

Top 10 Algorithms in Data Mining , X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. motoda, G.J. MClachlan, A. Ng, B. Liu, P.S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, Knowl Inf Syst (2008) 141-37.

Induction of Decision Trees , R. Quinlan, Machine Learning, 1(1):81-106, 1986.

Web and Link Mining

The Pagerank Citation Ranking: Bringing Order to the Web , L. Page, S. Brin, R. Motwani, T. Winograd, Technical Report, Stanford University, 1999.

The Structure and Function of Complex Networks , M. E. J. Newman, SIAM Review, 2003, 45, 167-256.

Link Mining: A New Data Mining Challenge , L. Getoor, SIGKDD Explorations, 2003, 5(1), 84-89.

Link Mining: A Survey , L. Getoor, SIGKDD Explorations, 2005, 7(2), 3-12.

Semi-supervised Learning

Semi-Supervised Learning Literature Survey , X. Zhu, Computer Sciences TR 1530, University of Wisconsin — Madison.

Introduction to Semi-Supervised Learning, in Semi-Supervised Learning (Chapter 1) O. Chapelle, B. Scholkopf, A. Zien (eds.), MIT Press, 2006. (Fordham’s library has online access to the entire text)

Learning with Labeled and Unlabeled Data , M. Seeger, University of Edinburgh (unpublished), 2002.

Person Identification in Webcam Images: An Application of Semi-Supervised Learning , M. Balcan, A. Blum, P. Choi, J. lafferty, B. Pantano, M. Rwebangira, X. Zhu, Proceedings of the 22nd ICML Workshop on Learning with Partially Classified Training Data , 2005.

Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains , N. Chawla, G. Karakoulas, Journal of Artificial Intelligence Research , 23:331-366, 2005.

Text Classification from Labeled and Unlabeled Documents using EM , K. Nigam, A. McCallum, S. Thrun, T. Mitchell, Machine Learning , 39, 103-134, 2000.

Self-taught Learning: Transfer Learning from Unlabeled Data , R. Raina, A. Battle, H. Lee, B. Packer, A. Ng, in Proceedings of the 24th International Conference on Machine Learning , 2007.

An iterative algorithm for extending learners to a semisupervised setting , M. Culp, G. Michailidis, 2007 Joint Statistical Meetings (JSM), 2007

Partially-Supervised Learning / Learning with Uncertain Class Labels

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers , V. Sheng, F. Provost, P. Ipeirotis, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2008.

Logistic Regression for Partial Labels , in 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems , Volume III, pp. 1935-1941, 2002.

Classification with Partial labels , N. Nguyen, R. Caruana, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2008.

Imprecise and Uncertain Labelling: A Solution based on Mixture Model and Belief Functions, E. Come, 2008 (powerpoint slides).

Induction of Decision Trees from Partially Classified Data Using Belief Functions , M. Bjanger, Norweigen University of Science and Technology, 2000.

Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth , P. Smyth, M. Burl, U. Fayyad, P. Perona, KDD Workshop 1994, AAAI Technical Report WS-94-03, pp. 109-120, 1994.

Recommender Systems

Trust No One: Evaluating Trust-based Filtering for Recommenders , J. O’Donovan and B. Smyth, In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), 2005, 1663-1665.

Trust in Recommender Systems, J. O’Donovan and B. Symyth, In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI-05), 2005, 167-174.

General resources available on this topic :

ICML 2003 Workshop: Learning from Imbalanced Data Sets II

AAAI ‘2000 Workshop on Learning from Imbalanced Data Sets

A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data , G. Batista, R. Prati, and M. Monard, SIGKDD Explorations , 6(1):20-29, 2004.

Class Imbalance versus Small Disjuncts , T. Jo and N. Japkowicz, SIGKDD Explorations , 6(1): 40-49, 2004.

Extreme Re-balancing for SVMs: a Case Study , B. Raskutti and A. Kowalczyk, SIGKDD Explorations , 6(1):60-69, 2004.

A Multiple Resampling Method for Learning from Imbalanced Data Sets , A. Estabrooks, T. Jo, and N. Japkowicz, in Computational Intelligence , 20(1), 2004.

SMOTE: Synthetic Minority Over-sampling Technique , N. Chawla, K. Boyer, L. Hall, and W. Kegelmeyer, Journal of Articifial Intelligence Research , 16:321-357.

Generative Oversampling for Mining Imbalanced Datasets, A. Liu, J. Ghosh, and C. Martin, Third International Conference on Data Mining (DMIN-07), 66-72.

Learning from Little: Comparison of Classifiers Given Little of Classifiers given Little Training , G. Forman and I. Cohen, in 8th European Conference on Principles and Practice of Knowledge Discovery in Databases , 161-172, 2004.

Issues in Mining Imbalanced Data Sets – A Review Paper , S. Visa and A. Ralescu, in Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference , pp. 67-73, 2005.

Wrapper-based Computation and Evaluation of Sampling Methods for Imbalanced Datasets , N. Chawla, L. Hall, and A. Joshi, in Proceedings of the 1st International Workshop on Utility-based Data Mining , 24-33, 2005.

C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , C. Drummond and R. Holte, in ICML Workshop onLearning from Imbalanced Datasets II , 2003.

C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure , N. Chawla, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Class Imbalances: Are we Focusing on the Right Issue?, N. Japkowicz, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Learning when Data Sets are Imbalanced and When Costs are Unequal and Unknown , M. Maloof, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Uncertainty Sampling Methods for One-class Classifiers , P. Juszcak and R. Duin, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Active Learning

Improving Generalization with Active Learning , D Cohn, L. Atlas, and R. Ladner, Machine Learning 15(2), 201-221, May 1994.

On Active Learning for Data Acquisition , Z. Zheng and B. Padmanabhan, In Proc. of IEEE Intl. Conf. on Data Mining, 2002.

Active Sampling for Class Probability Estimation and Ranking , M. Saar-Tsechansky and F. Provost, Machine Learning 54:2 2004, 153-178.

The Learning-Curve Sampling Method Applied to Model-Based Clustering , C. Meek, B. Thiesson, and D. Heckerman, Journal of Machine Learning Research 2:397-418, 2002.

Active Sampling for Feature Selection , S. Veeramachaneni and P. Avesani, Third IEEE Conference on Data Mining, 2003.

Heterogeneous Uncertainty Sampling for Supervised Learning , D. Lewis and J. Catlett, In Proceedings of the 11th International Conference on Machine Learning, 148-156, 1994.

Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , G. Weiss and F. Provost, Journal of Artificial Intelligence Research, 19:315-354, 2003.

Active Learning using Adaptive Resampling , KDD 2000, 91-98.

Cost-Sensitive Learning

Types of Cost in Inductive Concept Learning , P. Turney, In Proceedings Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning.

Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , P. Chan and S. Stolfo, KDD 1998.

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Trending Data Mining Research Papers

Data Mining (DM) is the pattern extraction and discovery process from large data sets. The unveiling of hidden knowledge and insights from a large volume of data involves data mining as its core and the most challenging and interesting step. Many applications, such as business, medicine, science, and engineering, have used data mining. It has led to numerous beneficial services to many walks of real businesses by both the providers and consumers of services. DM involves six common classes of tasks: Anomaly detection, association rule learning, clustering, classification, regression, and summarization. Applying existing data mining algorithms and techniques to real-world problems has recently faced many challenges due to inadequate scalability and other limitations. Current data mining techniques and algorithms are not ready to meet the new challenges of big data. Mining big data demands highly scalable strategies and algorithms and more effective preprocessing steps such as data filtering and integration, advanced parallel computing environments, and intelligent and effective user interactions. DM uses both new and legacy systems, which help businesses make informed decisions quickly and helps to detect credit risks and fraud, and also help data scientists easily to analyze enormous amounts of data quickly.

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Prediction of Skin Diseases Using Machine Learning

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

A Survey on Building Recommendation Systems Using Data Mining Techniques

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract

Diagnosis and detection of plant diseases using data mining techniques, location-based crime prediction using multiclass classification data mining techniques, an effective approach to test suite reduction and fault detection using data mining techniques.

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Dengue fever prediction modelling using data mining techniques, fake news detection using data mining techniques.

Nowadays, internet has been well known as an information source where the information might be real or fake. Fake news over the web exist since several years. The main challenge is to detect the truthfulness of the news. The motive behind writing and publishing the fake news is to mislead the people. It causes damage to an agency, entity or person. This paper aims to detect fake news using semantic search.

A Leading Indicator Approach with Data Mining Techniques in Analysing Bitcoin Market Value

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Top 10 Datamining Papers

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These papers provide a breadth of information about data mining that is generally useful and interesting from a Data science and Machine Learning perspective.

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Ahmed Bahgat B El Seddawy

Educational data mining is concerned with the development methods for exploring the unique types of data that come from the educational context. Furthermore, educational data mining is an emerging discipline that concerned with the developing methods for exploring the unique types of data that come from the educational context. This study focuses on the way of applying data mining techniques for higher education system by using the most common techniques on most common application called Moodle system in education system. There are an increasing numbers of researches that interest in using data mining in education system. The proposed system for Higher Educational Data Mining System (HEDMS) is concerned with the developing methods that discover useful knowledge from data that extracted from educational system. The data collated form historical and usage data reside in the databases of educational institutes. The proposed system helps to get sufficient results which consist of several steps in our case study starting with collected data, pre-processing, applying data mining techniques and visualization results. We collected students' data from Moodle database.

research papers related to data mining

Ahmed Bahgat B El Seddawy , Dr. Ayman khedr

Data mining have emerged to meet this need. They serve as an integrated repository for internal and external data-intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. The proposed system Investment Data Mining System (IDMS) will support top level management to make a good decision in any time under any uncertain environment and on another hand using enhancing K-mean algorithm.

Int. J. Advanced Networking and Applications

Prof. Mona Nasr

Diabetes is a inveterate defect and disturbance resulted from metabolic conk out in carbohydrate metabolism thus it has occupied a globally serious health problem. In general, the detection of diabetes in early stages can greatly has significant impact on the diabetic patients treatment in which lead to drive out its relevant side effects. Machine learning is an emerging technology that providing high importance prognosis and a deeper understanding for different clustering of diseases such as diabetes. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence 'BI' which is a data-driven Decision Support System. In this study, we proposed a high precision diagnostic analysis by using k-means clustering technique. In the first stage, noisy, uncertain and inconsistent data was detected and removed from data set through the preprocessing to prepare date to implement a clustering model. Then, we apply k-means technique on community health diabetes related indicators data set to cluster diabetic patients from healthy one with high accuracy and reliability results.

International Journal of Data Mining & Knowledge Management Process ( IJDKP ) , Dr. Ayman khedr

Decision Support System (DSS) is equivalent synonym as management information systems (MIS). Most of imported data are being used in solutions like data mining (DM). Decision supporting systems include also decisions made upon individual data from external sources, management feeling, and various other data sources not included in business intelligence. Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. Data mining have emerged to meet this need. They serve as an integrated repository for internal and external data-intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. The proposed system will support top level management to make a good decision in any time under any uncertain environment.

International Journal of Data Mining & Knowledge Management Process ( IJDKP ) , Sunita Jahirabadkar

Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high dimensional data. Many significant subspace clustering algorithms exist, each having different characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive classification scheme is essential which will consider all such characteristics to divide subspace clustering approaches in various families. The algorithms belonging to same family will satisfy common characteristics. Such a categorization will help future developers to better understand the quality criteria to be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithms’ Family). Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc. As an illustration, we further provided a comprehensive, systematic description and comparison of few significant algorithms belonging to “Axis parallel, overlapping, density based” SCAF.

Mustafa S. Kadhm

Diabetes prediction system is very useful system in the healthcare field. An accurate system for diabetes prediction is proposed in this paper. The proposed system used K-nearest neighbor algorithm for eliminating the undesired data, thus reducing the processing time. However, a proposed classification approach based on Decision Tree (DT) to assign each data sample to its appropriate class. By experiments, the proposed system achieved high classification result which is 98.7% comparing to the existing system using Pima Indians Diabetes (PID) dataset.

SN Computer Science, Springer Nature

Dr. Iqbal H. Sarker

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

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