HACS: Heuristic Algorithm for Clustering Subsets

The term consideration set is used in marketing to refer to the set of items a customer thought about purchasing before making a choice. While consideration sets are not directly observable, finding common ones is useful for market segmentation and choice prediction. We approach the problem of inducing common consideration sets as a clustering problem on the space of possible item subsets. Our algorithm combines ideas from binary clustering and itemset mining, and differs from other clustering methods by reflecting the inherent structure of subset clusters. Experiments on both real and simulated datasets show that our algorithm clusters effectively and efficiently even for sparse datasets. In addition, a novel evaluation method is developed to compare clusters found by our algorithm with known ones.

• Type: Conference
• Year: 2007
• Where: SDM
• Authors: Ding Yuan, W. Nick Street
• Keywords: Common Consideration Sets, Consideration Sets, Data Mining, SDM 2007, Term Consideration