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[1]蒋亦樟,朱丽,刘丽,等.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.[doi:10.11992/tis.201703031]
　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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多视角模糊双加权可能性聚类算法

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 12 期数: 2017年第6期页码: 806-815 栏目: 学术论文—机器学习出版日期: 2017-12-25

Title:: Multi-view fuzzy double-weighting possibility clustering algorithm

作者:: 蒋亦樟¹, 朱丽¹, 刘丽², 王士同¹; 1. 江南大学数字媒体学院, 江苏无锡 214122;
2. 江苏信息职业技术学院物联网工程学院, 江苏无锡 214153

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

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

Keywords:: multi-view clustering; fuzzy weighting between views; fuzzy weighting of attribute within views; possibilistic clustering

分类号:: TP181

DOI:: 10.11992/tis.201703031

摘要:: 为解决传统可能性聚类算法（PCM）无法满足多视角学习场景聚类的实际问题，并进一步考虑到现有多视角聚类算法尚未重视的视角权重及视角内特征权重优化问题，本文提出一种新的具备最佳视角及最优特征划分能力的多视角模糊双加权可能性聚类算法（MV-FDW-PCM）。该算法将基于传统的PCM算法，给出了详细的多视角聚类学习框架使得PCM算法具备多视角聚类能力，进而通过引入视角间模糊加权机制及视角内属性模糊加权机制解决视角间权重及视角内特征权重优化问题。实验结果表明，所提的MV-FDW-PCM算法在面对多视角聚类问题时较以往算法具有更佳的聚类效果。

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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备注/Memo

收稿日期:2017-03-23;改回日期:。
基金项目:国家自然科学基金项目（61300151，61702225）；江苏省自然科学基金项目（BK20160187）；中央高校基本科研业务费基金项目（JUSRP11737）.
作者简介:蒋亦樟,男,1988年生,讲师,博士,主要研究方向为人工智能、模式识别、模糊系统。发表学术论文40余篇,其中被SCI、EI检索20余篇;朱丽,女,1996年生,硕士研究生,主要研究方向为人工智能、模式识别、模糊系统;刘丽,女,1987年生,讲师,主要研究方向为人工智能、模式识别、模糊系统;王士同,男,1964年生,教授,博士生导师,主要研究方向为人工智能、模式识别和生物信息。发表学术论文百余篇,其中被SCI、EI检索50余篇。
通讯作者:蒋亦樟.E-mail:241519405@qq.com.

更新日期/Last Update: 2018-01-03

多视角模糊双加权可能性聚类算法 PDF下载HTML

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多视角模糊双加权可能性聚类算法

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