An Empirical Comparison of Latest Data Clustering Algorithms with State-of-the-Art

Abstract

Clustering technology has been applied in numerous applications. It can enhance the performance of information retrieval systems, it can also group Internet users to help improve the click-through rate of on-line advertising, etc. 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.

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity

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A Preference Model on Adaptive Affinity Propagation

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