TOP 10 DATAMINING PAPERS: RECOMMENDED READING – DATAMINING & KNOWLEDGEMENT MANAGEMENT RESEARCH

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    TOP 10 DATAMINING PAPERS: RECOMMENDED READING – DATAMINING & KNOWLEDGEMENT MANAGEMENT RESEARCH https://datam!!"t#p10pap$%s'#%dp%$ss(#m/  Citation Count – 85    Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012 Tipawan Silwattananusarn1 and Assoc.Prof. Dr. KulthidaTuamsuk2   1Ph.D. Student in Information Studies Program, Khon Kaen University, Thailand   2Head, Information & Communication Management Program, Khon Kaen University, Thailand   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 For More Details : http://aircconline.com/ijdkp/V2N5/2512ijdkp02.pdf    Volume Link : http://airccse.org/journal/ijdkp/vol2.html   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 [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/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 Citation Count – 83   Analysis of Heart Diseases Dataset Using Neural Network Approach   Dr. K. Usha Rani   Dept. of Computer Science, Sri Padmavathi Mahila Visvavidyalayam (Women’s University), Tirupati - 517502 , Andhra Pradesh, India   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 For More Details : http://aircconline.com/ijdkp/V1N5/0911ijdkp01.pdf    Volume Link : http://airccse.org/journal/ijdkp/vol1.html   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.
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