Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CLOPE: a fast and effective clustering algorithm for transactional data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
Discovering pattern-based subspace clusters by pattern tree
Knowledge-Based Systems
Similarity search in transaction databases with a two-level bounding mechanism
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Goal attainment on long tail web sites: An information foraging approach
Decision Support Systems
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Grouping customer transactions into categories helpsunderstand customers better. The marketing literaturehas concentrated on identifying important segmentationvariables (e.g. customer loyalty) and on using clusteringand mixture models for segmentation. The data miningliterature has provided various clustering algorithms forsegmentation. In this paper we investigate using"pattern-based" clustering approaches to groupingcustomer transactions. We argue that there are clustersin transaction data based on natural behavioral patterns,and present a new technique, YACA, that groupstransactions such that itemsets generated from eachcluster, while similar to each other, are different fromones generated from others. We present experimentalresults from user-centric Web usage data thatdemonstrates that YACA generates a highly effectiveclustering of transactions.