Commenced in January 2007
Paper Count: 31430
Moving Data Mining Tools toward a Business Intelligence System
Abstract:Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330819Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1418
 E. Awad and H. Ghaziri, Knowledge Management, Pearson Prentice Hall, 2004.
 R. Bird, Introduction to Functional Programming using Haskell, Prentice Hall, 1998.
 U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, ''From data mining to knowledge discovery: An Overview,'' in Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996.
 P. Hudak, J. Fasel, and J. Peterson, ''A gentle introduction to Haskell,'' Yale University, Technical Report Yale U/DCS/RR-901, 1996.
 K. Kerdprasop and N. Kerdprasop, ''Multi-agents in data filtering systems,'' in Proc. 7th Int. Conf. on Software Engineering and Applications, 2003, pp.471-475.
 N. Kerdprasop and K. Kerdprasop, ''Enhancing the power of OLAP with knowledge discovery,'' in Proc. 7th Int. Conf. on Software Engineering and Applications, 2003, pp.43-47.
 M. Raisinghani (ed.), Business Intelligence in the Digital Economy, Idea Group Publishing, 2004.
 K. Rennolls, ''An intelligent framework (O-SS-E) for data mining, knowledge discovery and business intelligence,'' in Proc. 16th Int. Workshop on Database and Expert System Applications, 2005, pp.715-719.
 R. Roiger and M. Geatz, Data Mining: A Tutorial-Based Primer, Addison Wesley, 2003.
 WEKA, available at http://www.cs.waikato.ac.nz/ml/weka
 I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.), Morgan Kaufmann, 2005.