A New Assessment of Cluster Tendency Ensemble approach for Data Clustering

Abstract

The ensemble is an universal machine learning method that is based on the divide-and-conquer principle. The ensemble aims to improve performance of system in terms of processing speed and quality. The assessment of cluster tendency is a method determining whether a considering data-set contains meaningful clusters. Recently, a silhouette-based assessment of cluster tendency method (SACT) has been proposed to simultaneously determine the appropriate number of data clusters and the prototypes. The advantages of SACT are accuracy and less the parameter, while there are limitations in data size and processing speed. In this paper, we proposed an improved SACT method for data clustering. We call eSACT algorithm. Experiments were conducted on synthetic data-sets and color image images. The proposed algorithm exhibited high performance, reliability and accuracy compared to previous proposed algorithms in the assessment of cluster tendency.