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- W2130525844 abstract "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. 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." @default.
- W2130525844 created "2016-06-24" @default.
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- W2130525844 date "2004-04-23" @default.
- W2130525844 modified "2023-09-22" @default.
- W2130525844 title "Segmenting customer transactions using a pattern-based clustering approach" @default.
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- W2130525844 doi "https://doi.org/10.1109/icdm.2003.1250947" @default.
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