An extension to Rough c-means clustering based on decision-theoretic Rough Sets model

被引：37

作者：

Li, Fan ^{[1
]}

Ye, Mao ^{[1
]}

Chen, Xudong ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China

[2] Chongqing Technol & Business Univ, Sch Comp Sci & Informat Engn, Chongqing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2014年 / 55卷 / 01期

基金：

美国国家科学基金会;

关键词：

Decision theory; Loss function; Rough c-means clustering; Decision-theoretic Rough Sets model; SHADOWED SETS; K-MEANS; FUZZY; ALGORITHM;

D O I：

10.1016/j.ijar.2013.05.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rough c-means algorithm has gained increasing attention in recent years. However, the assignment scheme of Rough c-means algorithm does not incorporate any information about the neighbors of the data point to be assigned and may cause undesirable solutions in practice. This paper proposes an extended Rough c-means clustering algorithm based on the concepts of decision-theoretic Rough Sets model. In the risk calculation, a new kind of loss function is utilized to capture the loss information of the neighbors. The assignment scheme of the present multi-category decision-theoretic Rough Sets model is also adjusted to deal with the potentially high computational cost. Experimental results are provided to validate the effectiveness of the proposed approach. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：116 / 129

页数：14

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