A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes

被引：10
作者：
Cao, Fuyuan [1 ]
Huang, Joshua Zhexue [2 ]
Liang, Jiye [1 ]
机构：
[1] Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Peoples R China
[2] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2017年 / 295卷
基金：
中国国家自然科学基金;
关键词：
Categorical data; Set-valued attribute; Set-valued modes; Fuzzy k-modes; Fuzzy SV-k-modes;
D O I：
10.1016/j.amc.2016.09.023
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we propose a fuzzy SV-k-modes algorithm that uses the fuzzy k-modes clustering process to cluster categorical data with set-valued attributes. In the proposed algorithm, we use Jaccard coefficient to measure the dissimilarity between two objects and represent the center of a cluster with set-valued modes. A heuristic update way of cluster prototype is developed for the fuzzy partition matrix. These extensions make the fuzzy SV-k-modes algorithm can cluster categorical data with single-valued and set-valued attributes together and the fuzzy k-modes algorithm is its special case. Experimental results on the synthetic data sets and the three real data sets from different applications have shown the efficiency and effectiveness of the fuzzy SV-k-modes algorithm. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码：1 / 15
页数：15
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