Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
In this paper, we discuss a technique 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.
Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
Ruixiong Tian, Zhe Xiang, et al.
Qinghua Daxue Xuebao/Journal of Tsinghua University
Elliot Linzer, M. Vetterli
Computing
Renu Tewari, Richard P. King, et al.
IS&T/SPIE Electronic Imaging 1996