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

页码：1988 / 1993

页数：6

共 14 条

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