智能系统学报2017，Vol.12Issue(6)：806-815,10.DOI:10.11992/tis.201703031
多视角模糊双加权可能性聚类算法
Multi-view fuzzy double-weighting possibility clustering algorithm
摘要
Abstract
To solve the problem that traditional possibility clustering algorithms (PCM) barely achieve multi-view clus-tering, 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 mechan-ism 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.关键词
多视角聚类/视角间模糊加权/视角内属性模糊加权/可能性聚类Key words
multi-view clustering/fuzzy weighting between views/fuzzy weighting of attribute within views/possibil-istic clustering分类
信息技术与安全科学引用本文复制引用
蒋亦樟,朱丽,刘丽,王士同..多视角模糊双加权可能性聚类算法[J].智能系统学报,2017,12(6):806-815,10.基金项目
国家自然科学基金项目(61300151,61702225) （61300151,61702225）
江苏省自然科学基金项目(BK20160187) （BK20160187）
中央高校基本科研业务费基金项目(JUSRP11737). （JUSRP11737）
