Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Detecting Temporally Redundant Association Rules
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
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Identifying customer segments and tracking their change over time is an important application for enterprises who need to understand what their customers expect from them. Customer segmentation is typically done by applying some form of cluster analysis. In this paper we present an alternative approach based on associaton rule mining and a notion of interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Our approach is assumption-free and pro-active and can be run continuously. Newly discovered segments or relevant changes will be reported automatically based on the application of an interestingness measure.