Grouping customer transactions into categories helps understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g. customer loyalty) and on using clustering and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation. In this paper we investigate using "pattern-based" clustering approaches to grouping customer transactions. We argue that there are clusters in transaction data based on natural behavioral patterns, and present a new technique, YACA, that groups 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 YACA generates a highly effective clustering of transactions.