TY - JOUR
T1 - A comparative analysis on the bisecting K-means and the PDDP clustering algorithms
AU - Savaresi, Sergio M.
AU - Boley, Daniel L.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
KW - K-means
KW - principal direction divisive partitioning
KW - unsupervised clustering
UR - http://www.scopus.com/inward/record.url?scp=41849149791&partnerID=8YFLogxK
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U2 - 10.3233/ida-2004-8403
DO - 10.3233/ida-2004-8403
M3 - Article
AN - SCOPUS:41849149791
VL - 8
SP - 345
EP - 362
JO - Intelligent Data Analysis
JF - Intelligent Data Analysis
SN - 1088-467X
IS - 4
ER -