Cluster Selection in Divisive Clustering Algorithms

The problem this paper focuses on is the classical problem of unsupervised clustering of a data-set. In particular, the bisecting divisive clustering approach is here considered. This approach consists in recursively splitting a cluster into two sub-clusters, starting from the main data-set. This is one of the more basic and common problems in fields like pattern analysis, data mining, document retrieval, image segmentation, decision making, etc. ([13], [15]). Note that by recursively using a bisecting divisive clustering procedure, the data-set can be partitioned into any given number of clusters. Interestingly enough, the so-obtained clusters are structured as a hierarchical binary tree (or a binary taxonomy). This is the reason why the bisecting divisive approach is very attractive in many applications (e.g. in document-retrieval/indexing problems – see e.g. [23]). Any divisive clustering algorithm can be divided into two sub-problems: • the problem of selecting which cluster must be split; • the problem of how splitting the selected cluster. This paper focuses on the first sub-problem. In particular, in this paper a new method for the selection of the cluster to split is proposed. This method is here presented with