A new topological clustering algorithm for interval data

•We propose a clustering algorithm for interval data, based on a Self-Organizing Map.•Its major advantage is that the number of clusters to find is detected automatically.•The results confirm the effectiveness of the algorithm to deal with interval data.•The algorithm discriminates perfectly overlapped groups of different shapes.

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.