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)|
|Number of pages||18|
|Journal||Intelligent Data Analysis|
|State||Published - Dec 1 2004|
- principal direction divisive partitioning
- unsupervised clustering