In this paper, we discuss a techniques for discovering localized associations in segments of the data using clustering. Often the aggregate behavior of a data set may be very different from localized segments. In such cases, it is desirable to design algorithms which are effective in discovering localized associations, because they expose a customer pattern which is more specific than the aggregate behavior. This information may be very useful for target marketing. We present empirical results which show that the method is indeed able to find a significantly larger number of associations than what can be discovered by analysis of the aggregate data.
By: Charu C. Aggarwal, Cecilia Procopiuc, Philip S. Yu
Published in: RC21594 in 1999
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