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Mobile Information Systems
Volume 2016, Article ID 4356127, 8 pages
http://dx.doi.org/10.1155/2016/4356127
Research Article

A High-Order CFS Algorithm for Clustering Big Data

1School of Software Technology, Dalian University of Technology, Dalian 116620, China
2School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
3Department of Student Work, Southwest University, Chongqing 400715, China
4College of Business Administration, Dalian University of Finance and Economics, Dalian 116622, China

Received 6 May 2016; Accepted 26 June 2016

Academic Editor: Beniamino Di Martino

Copyright © 2016 Fanyu Bu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.