Affiliations: [a] Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México | [b] Centro de Bioplantas, Universidad de Ciego de Ávila, Ciego de Ávila, Cuba | [c] Instituto Superior Politécnico José Antonio Echeverría, La Habana, Cuba
Correspondence:
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Corresponding author: A.E. Gutierrez-Rodríguez, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, México. E-mail:[email protected]
Abstract: 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.
Keywords: Pattern-based clustering, frequent patterns, unsupervised decision trees
