ABSTRACT

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.

Keywords

Data mining; Data mining applications; Knowledge management

REFERENCES

[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] Cantú, 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

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[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/j.im.2003.06.004

[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

ABSTRACT

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.

Keywords

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

REFERENCES

[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.

[8] B.Widrow, D. E. Rumelhard, and M. A. Lehr, (1994) “Neural networks: Applications in industry, business and science,” Commun. ACM, vol. 37, pp.93–105.

[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: http://www.cs.rpi.edu/~bivenj/MRC/proceedings/papers/researchpaper.pdf

[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.

ABSTRACT

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.

Keywords

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

REFERENCES

[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, http://ellislab.com/codeigniter/user-guide/toc.html

[7] RapidMiner, http://rapid-i.com/content/view/181/190/

[8] MySQL – The world’s most popular open source database, http://www.mysql.com/

ABSTRACT

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 Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

Keywords

Classification, Data Mining, Decision Tree, Diabetes and Naïve Bayes.

REFERENCES

[1] National Diabetes Information Clearinghouse (NDIC),
http://diabetes.niddk.nih.gov/dm/pubs/type1and2/#signs

[2] Global Diabetes Community, http://www.diabetes.co.uk/diabetes_care/blood-sugar-level-ranges.html

[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)

ABSTRACT

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.

Keywords

Clustering, data mining, intrusion detection, network security

REFERENCES

[1] J. Anderson, “Computer security threat monitoring and surveillance”, 1980.

[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.http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999.

ABSTRACT

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.

Keywords

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

REFERENCES

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[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, Incremental 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.

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ABSTRACT

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

Keywords

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

REFERENCES

[1] Power, D.J., (2002) Decision Support Systems: Concepts and Resources for Managers. Quorum Books/Greenwood Publishing.

[2] Han, J. and M. Kamberm (2006). Data mining: concepts and techniques. Amsterdam; Boston San Francisco, CA, Elsevier; Morgan Kaufmann.

[3] Clark, R. C., & Mayer, R. E., (2003). e-Learning and the Science of Instruction: Proven
Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Jossey-Bass.

[4] Kamber, M., Winstone, L., Gong, W., Cheng, S. and Han, J. (1997). Generalization and decision tree induction: efficient classification in data mining. In 7th International Workshop on Research Issues in Data Engineering (RIDE ’97) High Performance Database Management for Large-Scale Applications, pp.111.

[5] Agrawal, R., Imielinski,T. and Swami, A., (1993), Mining association rules between sets of items in large databases In Prooc. of the ACM SIGMOD Int’l Conf. on Management of Data (ACM SIGMOD ’93), Washington, USA.

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[7] Russell S., Peter Norvig, P., (2010), Artificial intelligence: a modern approach, 3rd edition, Prentice Hall.

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[21]Marta Zorrilla, Diego García and Elena Álvarez.(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).

[22]Roberto Llorente and Maria Morant, (2011), Data Mining in Higher Education, Kimito Funatsu, InTech, 2011.

[23]Falakmasir M., and Habibi J., (2010), Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning, Educational Data Mining 2010, 3rd International Conference on Educational Data Mining , Pittsburgh, PA, USA, June 11-13, 2010.

[24]Rajibussalim M., (2010), Mining Students’ Interaction Data from a System that Support Learning by Reflection, Educational Data Mining 2010, 3rd International Conference on Educational Data Mining , Pittsburgh, PA, USA, June 11-13, 2010.

[25]Kumar R. and Chadrasekaran R.,(2011), Attribute Correction – Data Cleaning Using Association Rule and Clustering Methods, International Journal of Data Mining & Knowledge Management Process (IJDKP). Vol(1),No(2).

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ABSTRACT

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.

Keywords

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

REFERENCES

[1] Ran Vijay Singh and M.P.S Bhatia , “Data Clustering with Modified K-means Algorithm”, IEEE International Conference on Recent Trends in Information Technology, ICRTIT 2011, pp 717-721.

[2] D. Napoleon and P. Ganga lakshmi, “An Efficient K-Means Clustering Algorithm for Reducing Time Complexity using Uniform Distribution Data Points”, IEEE 2010.

[3] Tajunisha and Saravanan, “Performance Analysis of k-means with different initialization methods for high dimensional data” International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.4, October 2010

[4] Neha Aggarwal and Kriti Aggarwal,”A Mid- point based k –mean Clustering Algorithm for Data Mining”. International Journal on Computer Science and Engineering (IJCSE) 2012.

[5] Barileé 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.

[7] Ahamad Shafeeq and Hareesha ”Dynamic clustering of data with modified K-mean algorithm”, Proc. International Conference on Information and Computer Networks (ICICN 2012) IPCSIT vol. 27 (2012) © (2012) IACSIT Press, Singapore 2012.

[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.

[12] A Review of Data Clustering Approaches Vaishali Aggarwal, Anil Kumar Ahlawat, B.N Panday. ISSN: 2277-3754 International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 4, April 2012.

[13] Ali Alijamaat, Madjid Khalilian, and Norwati Mustapha, “A Novel Approach for High Dimensional Data Clustering” 2010 Third International Conference on Knowledge Discovery and Data Mining.

[14] Zhong Wei, et al. “Improved K-Means Clustering Algorithm for Exploring Local Protein Sequence Motifs Representing Common Structural Property” IEEE Transactions on Nanobioscience, Vol.4., No.3. Sep. 2005. 255-265.

[15] K.A.Abdul Nazeer, M.P.Sebastian, “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm”,Proceeding of the World Congress on Engineering, vol 1,london, July 2009.

[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.

ABSTRACT

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

Keywords

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

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[68] Case study by Zap Technology, (2010) “KFC/Pizza Hut makes efficiency gains with Zap Business Intelligence: Businesses become more agile, responsive and performance-focused”.

ABSTRACT

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.

Keywords

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

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