Mining patterns for clustering using unsupervised decision trees

In clustering, providing an explanation of the results is an important task. Pattern-based clustering algorithms return a set of patterns that describe the objects grouped in each cluster. The most recent algorithms proposed in this approach have a high computational cost in the clustering stage, making them non suitable when a huge amount of patterns are extracted from a dataset. In this paper, we introduce an algorithm for extracting a small subset of patterns useful for clustering. The proposed algorithm extracts patterns from a collection of trees generated through a new induction procedure. Experimental results show that the proposed algorithm extracts significantly less patterns in a significantly less time than recent pattern-based clustering algorithms, but obtaining similar clustering results in terms of F-measure. It makes our algorithm suitable for medium-large datasets where other pattern-based clustering algorithms cannot produce a result in a reasonable time. In addition, our algorithm obtains similar clustering quality results than traditional clustering algorithms.