A new topological clustering algorithm for interval data

作者：Cabanes Guenael^*; Bennani Younes; Destenay Renaud; Hardy Andre

来源：Pattern Recognition, 2013, 46(11): 3030-3039.

DOI：10.1016/j.patcog.2013.03.023

• Interval data
• Clustering
• Self-organizing map

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.

• 出版日期2013-11