Mining evolving customer-product relationships in multi-dimensional space

Previous work on mining transactional database has focused primarily on mining frequent itemsets, association rules, and sequential patterns. However, interesting relationships between customers and items, especially their evolution with time, have not been studied thoroughly. In this paper we propose a Gaussian transformation-based regression model that captures time-variant relationships between customers and products. Moreover, since it is interesting to discover such relationships in a multi-dimensional space, an efficient method has been developed to compute multi-dimensional aggregates of such curves in a data cube environment. Our experimental results have demonstrated the promise of the approach.