Abstract
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 and discussed; for the 2-dimensional case a closed-form model is given.
Original language | English (US) |
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Pages (from-to) | 345-362 |
Number of pages | 18 |
Journal | Intelligent Data Analysis |
Volume | 8 |
Issue number | 4 |
DOIs | |
State | Published - 2004 |
Keywords
- K-means
- principal direction divisive partitioning
- unsupervised clustering