Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. In this paper we focus on interval data: i.e., where the objects are defined as hyper-rectangles. We propose here a new clustering algorithm for interval data, based on the learning of a Self-Organizing Map. The major advantage of our approach is that the number of clusters to find is determined automatically; no a priori hypothesis for the number of clusters is required. Experimental results confirm the effectiveness of the proposed algorithm when applied to interval data.
|Pages (de - à)||3030-3039|
|Nombre de pages||10|
|Numéro de publication||11|
|Etat de la publication||Publié - 1 nov. 2013|