Bisecting K-means and PDDP: A Comparative Analysis
نویسندگان
چکیده
This paper deals with the problem of clustering a data−set. In particular, the bisecting divisive partitioning approach is here considered. We focus on two algorithms: the celebrated K−means algorithm, and the recently proposed Principal Direction Divisive Partitioning (PDDP) algorithm. A comparison of the two algorithms is given, under the assumption that the data set is uniformly distributed within an ellipsoid. In particular, the dynamic behavior of the K−means iterative procedure is studied; for the 2− dimensional case a closed−form model is given.
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