Outlier-robust multi-view clustering for uncertain data

Nowadays, multi-view clustering is drawn more and more attention in the area of machine learning because real-world datasets frequently consist of multiple views. Moreover, it provides complementary and consensus information across multiple views. So, owing to the efficacy of revealing the concealed patterns in uncertain data, multiple views are considered in this study. But, a multi-view clustering algorithm is not alone sufficient to increase accuracy. A similarity measure is equally important in uncertain data clustering. However, existing similarity functions for clustering uncertain data afflict with several problems. Geometric distance-based similarity function cannot correctly capture the change between uncertain data with their distributions when they are massively location-wise overlapped. On the other hand, the divergence-based similarity function cannot discriminate against the change between various duos of absolutely disjointed uncertain data. Thus, a self-adaptive mixture similarity function based on geometric distance and S-divergence is introduced for uncertain data clustering. The proposed similarity function is integrated with k-medoids based multi-view clustering. The proposed method reduces the effect of outliers and noises since it uses the threshold-based residual objective function in k-medoids. Finally, extensive experimental results on synthetic and real-world uncertain datasets illustrate that the proposed method consistently defeats the state-of-the-art clustering algorithms. Experimental results also demonstrate the effectiveness and robustness of the proposed method against noise and outliers. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Multi-view clustering is gaining more attention due to the presence of multiple views in real-world datasets, providing complementary and consensus information. An adaptive mixture similarity function based on geometric distance and S-divergence is introduced for uncertain data clustering, integrated with k-medoids to reduce the impact of outliers and noises. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method against noise and outliers.