Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A new method for ranking changes in customer's behavioral patterns in department stores
Proceedings of the 11th International Conference on Electronic Commerce
A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Editorial: Data mining for understanding user needs
ACM Transactions on Computer-Human Interaction (TOCHI)
Efficient classification based on multi-scale traffic data extraction patterns of cellular networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Segmenting customers by transaction data with concept hierarchy
Expert Systems with Applications: An International Journal
Customer behavior analysis using rough set approach
Journal of Theoretical and Applied Electronic Commerce Research
| Hi-index | 12.05 |
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 - now and in the future. This in particular is important for businesses which operate in dynamic markets with customers who, driven by new innovations and competing products, have highly changing demands and attitudes. Customer segmentation is typically done by applying some form of cluster analysis to obtain a set of segments to which future customers are assigned to. In this paper, we present a system for customer segmentation which accounts for the dynamics of today's markets. It employs an approach based on the discovery of frequent itemsets and the analysis of their change over time which, finally, results in a change-based notion of segment interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Thereby, 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 several interestingness measures.