[1]JIANG Yizhang,ZHU Li,LIU Li,et al.Multi-view fuzzy double-weighting possibility clustering algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(6):806-815.[doi:10.11992/tis.201703031]
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Multi-view fuzzy double-weighting possibility clustering algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 806-815 Column: 学术论文—机器学习 Public date: 2017-12-25
Title:
Multi-view fuzzy double-weighting possibility clustering algorithm
Author(s):
JIANG Yizhang1;  ZHU Li1;  LIU Li2;  WANG Shitong1
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi 214153, China
Keywords:
multi-view clustering; fuzzy weighting between views; fuzzy weighting of attribute within views; possibilistic clustering
CLC:
TP181
DOI:
10.11992/tis.201703031
Abstract:
To solve the problem that traditional possibility clustering algorithms (PCM) barely achieve multi-view clustering, and considering that the optimization of views and feature weights has not been regarded as important in existing multi-view clustering algorithms, this paper proposes a new multi-view fuzzy double-weighted possibility clustering algorithm (MV-FDW-PCM). The algorithm is based on the traditional PCM algorithm, and it gives a detailed multi-view clustering learning framework, which gives it its own multi-view clustering ability. It realizes the optimization of the weight of view and the feature weight within the view by the introduction of an inter-view fuzzy weighting mechanism and an inside-view attribute fuzzy weighting mechanism. The experimental results show that the proposed MV-FDW-PCM algorithm has better clustering performance than the previous algorithms regarding multi-view clustering.
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