Comparison of Imputation Strategies in FNM-based and RFCM-based Fuzzy Co-Clustering

被引：0
|
作者：
Kanzawa, Yuchi [1 ]
机构：
[1] Shibaura Inst Technol, Tokyo 1358548, Japan
来源：
6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS | 2012年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, some imputation strategies are compared in the point that the block diagonal part of the augmented dissimilarity matrix must be filled in for FNM-based and RFCM-based fuzzy co-clustering by entropy regularization, By numerical experiment, the eRFCM-based method with the minimax version of the strategy of the triangle inequality-based approximation and with higher fuzzifier parameter setting achieves the higher value of the normalized mutual information than others.
引用
收藏
页码：1988 / 1993
页数：6
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