Abstract
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.
Original language | English |
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Pages (from-to) | 3030-3039 |
Number of pages | 10 |
Journal | Pattern Recognition |
Volume | 46 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2013 |
Keywords
- Clustering
- Interval data
- Self-organizing map