C4.5: programs for machine learning
C4.5: programs for machine learning
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Visualizing customer segmentations produce by self organizing maps (case study)
VIS '97 Proceedings of the 8th conference on Visualization '97
ACM Computing Surveys (CSUR)
High performance computing with the Array package for Java: a case study using data mining
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Data Mining and Knowledge Discovery
Parallel and Distributed Data Mining: An Introduction
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Java programming for high-performance numerical computing
IBM Systems Journal
Data mining for knowledge management in technology enhanced learning
AEE'07 Proceedings of the 6th conference on Applications of electrical engineering
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Discovering frequent itemsets using transaction identifiers
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Monthly rainfall estimation using data-mining process
Applied Computational Intelligence and Soft Computing
Matching Observed with Empirical Reality --What you see is what you get?
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
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TO COMPETE EFFECTIVELY IN today's marketplace, business managers must take timely advantage of high-return opportunities. Doing so requires that they be able to exploit the mountains of data their organizations generate and collect during daily operations. Yet, the difficulty of discerning the value in that information--of separating the wheat from the chaff--prevents many companies from fully capitalizing on the wealth of data at their disposal.For example, a bank account manager might want to identify a group of married, two-income, affluent customers and send them information about the bank's growth mutual funds, before a competing discount broker can lure them away. The information surely resides in the bank's computer system--and has probably been there in some form for years. The trick, of course, is to find an efficient way to extract and apply it.Data mining -- the process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions--currently performs this task for a growing range of businesses. After presenting an overview of current data-mining techniques, this article explores two particularly noteworthy applications of those techniques: market basket analysis and customer segmentation.