Mining patterns for clustering on numerical datasets using unsupervised decision trees

Pattern-based clustering algorithms return a set of patterns that describe the objects of each cluster. The most recent algorithms proposed in this approach extract patterns on numerical datasets by applying an a priori discretization process, which may cause information loss. In this paper, we introduce a new pattern-based clustering algorithm for numerical datasets, which does not need an a priori discretization on numerical features. The new algorithm extracts, from a collection of trees generated through a new induction procedure, a small subset of patterns useful for clustering. Experimental results show that the patterns extracted by the proposed algorithm allows to build a pattern-based clustering algorithm, which obtains better clustering results than recent pattern-based clustering algorithms. In addition, the proposed algorithm obtains similar clustering results, in quality, than traditional clustering algorithms.