PUMA
Istituto di Matematica Applicata e Tecnologie Informatiche     
Savaresi S., Boley D. L., Bittanti S., Gazzaniga G. Cluster selection in divisive clustering algorithms. Preprint ercim.cnr.ian//2002-1267, 2002.
 
 
Abstract
(English)
This paper deals with the problem of clustering a data-set. In particular, the bisecting divisive approach is here considered. This approach can be naturally divided into two sub-problems: the problem of choosing which cluster must be divided, and the problem of splitting the selected cluster. The focus here is on the first problem. The contribution of this work is to propose a new technique for the selection of the cluster to split. This technique is based upon the shape of the cluster. This result is presented with reference to two specific splitting algorithms: the celebrated bisecting K-means algorithm, and the recently proposed Principal Direction Divisive Partitioning (PDDP) algorithm. The problem of evaluating the quality of a partition is also discussed.
Subject Data mining
Clustering algorithms
Pattern analysis
G.4 . Mathematical Software
H.3.3 . Information Search and Retrieval
I.5.3 . Clustering
62-07 Data Analysis
68P10 Searching and Sorting


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