Bisecting K-means and PDDP: A Comparative Analysis
2000-09-28
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Bisecting K-means and PDDP: A Comparative Analysis
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2000-09-28
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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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Savaresi, Sergio M.; Boley, Daniel. (2000). Bisecting K-means and PDDP: A Comparative Analysis. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215435.
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