A Clustering Algorithm based on Internal Constrained Multi-view K-means

As more and more multi-view data are collected, how to apply the traditional clustering algorithm to multi-view data has been studied widely. Among them, the K-means clustering algorithm is extended because of its efficiency on large-scale datasets. Based on the K-means clustering algorithm and the multi-view data without domain knowledge, this paper presents a clustering algorithm based on internal constrained multi-view K-means (ICMK). This paper also evaluates the proposed method on three standard datasets (digits dataset, IS dataset, WTP dataset), and compares with some baseline methods. The experiment results show that ICMK can produce a good view interaction structure automatically and higher quality clustering results.

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