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Wanwan Wang
Henan University

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An Empirical Comparison of Latest Data Clustering Algorithms with State-of-the-Art Xianjin Shi; Wanwan Wang; Chongsheng Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 2: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i2.pp410-415

Abstract

Over the past few decades, a great many data clustering algorithms have been developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In recent years, two new data  clustering algorithms have been proposed, which are affinity propagation (AP, 2007) and density peak based clustering (DP, 2014). In this work, we empirically compare the performance of these two latest data clustering algorithms with state-of-the-art, using 6 external and 2 internal clustering validation metrics. Our experimental results on 16 public datasets show that, the two latest clustering algorithms, AP and DP, do not always outperform DBSCAN. Therefore, to find the best clustering algorithm for a specific dataset, all of AP, DP and DBSCAN should be considered.  Moreover, we find that the comparison of different clustering algorithms is closely related to the clustering evaluation metrics adopted. For instance, when using the Silhouette clustering validation metric, the overall performance of K-Means is as good as AP and DP. This work has important reference values for researchers and engineers who need to select appropriate clustering algorithms for their specific applications.
Co-Authors Chongsheng Zhang Xianjin Shi