Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0

Yi Gu,Kang Li,Shan Zhong

doi:10.1155/2021/9963133

Abstract

In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.

Highlights

In the era of Industry 4.0, as the methods of data collection become more and more diverse, the complexity of data is increasing
Multiple points of view are introduced on the basis of the classical fuzzy C-means (FCM) algorithm using the Havrda-Charvat entropy structure of different view space approaching
The proposed Co-MVFCM method can better find out the similarities between view compositions, and from the view of entropy approximation of a different view space, reasonable physical explanation, and get more guiding significance to the overall space partition

Summary

Introduction

In the era of Industry 4.0, as the methods of data collection become more and more diverse, the complexity of data is increasing. Through the summary of the current research on multiview clustering, we find that the current research mainly focuses on the following aspects: (1) the early multiview clustering algorithm usually preprocesses the data itself, and the most direct method is to synthesize a multiview data into a single-view data through feature fusion and use the data clustering analysis; (2) most of the multiview clustering algorithms proposed in recent years use collaborative learning strategy, which can enhance the performance of each view data in the process of clustering; (3) when most multiview clustering algorithms with collaborative learning ability treat each view, their common practice is to average the weight of each view. A new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM is proposed by combining with the Havrda-Charvat entropy and fuzzy index. Our proposed Co-MVFCM algorithm has good space division ability and has the ability to adaptively recognize the best view

Related Work

Experimental Studies

Conclusion