Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
CLOPE: a fast and effective clustering algorithm for transactional data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Minimizing Information Loss and Preserving Privacy
Management Science
An efficient hierarchical clustering model for grouping web transactions
International Journal of Business Intelligence and Data Mining
The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
An approach for temporal analysis of email data based on segmentation
Data & Knowledge Engineering
Discovering pattern-based subspace clusters by pattern tree
Knowledge-Based Systems
Toward user patterns for online security: Observation time and online user identification
Decision Support Systems
Web user behavioral profiling for user identification
Decision Support Systems
A fuzzy bi-clustering approach to correlate web users and pages
International Journal of Knowledge and Web Intelligence
Discovering Knowledge-Sharing Communities in Question-Answering Forums
ACM Transactions on Knowledge Discovery from Data (TKDD)
A practical approach for clustering transaction data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GHIC, that groups customer transactions such that itemsets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions.