Abstract
Clustering data with both numeric and categorical attributes is of great importance as such data are ubiquitous in real-world problems. Multi-view learning approaches have proven to be more effective and having better generalisation ability compared to single-view learning in many problems. However, most of the existing clustering algorithms developed for mixed numeric and categorical data are single-view. In this research, we propose a novel multi-view clustering algorithm based on the k-prototypes (which we term Multi-view K-Prototypes) for clustering mixed data. To the best of our knowledge, our proposed Multi-view K-Prototypes is the first multi-view version of the well-known k-prototypes algorithm. To cluster the mixed data over multiple views, we present a novel representation prototype of cluster centres in the scenario of multiple views, and we also devise formulas for updating the cluster centres over each view. Then we propose the concept of consensus cluster centres to output the final clustering result. Finally, we carried out a series of experiments on four benchmark datasets to assess the performance of the proposed Multi-view K-Prototypes clustering. Experimental results show that the Multi-view K-Prototypes algorithm outperforms the seven state-of-the-art algorithms in most cases.
Highlights
Clustering analysis, which identifies the nature groups of data objects in an unsupervised manner, is a fundamental task in data mining and machine learning [1]–[4]
In this article we present a novel multi-view clustering algorithm based on the k-prototypes framework (Multiview K-Prototypes) for mixed numeric and categorical data
THE PROPOSED METHOD we first develop the representation and updating approaches of cluster centres in the multi-view scenario, and we present the concept of consensus prototype to output the final clustering result
Summary
JINCHAO JI 1,2,3, RUONAN LI1,2,3, WEI PANG4,5, FEI HE 1,2,3, GUOZHONG FENG 1,2,3, AND XIAOWEI ZHAO 1,2,3.
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