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Advances in Fuzzy Systems
Volume 2011, Article ID 265170, 10 pages
Research Article

A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data

Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka 599-8531, Japan

Received 9 June 2011; Revised 28 July 2011; Accepted 31 July 2011

Academic Editor: Salvatore Sessa

Copyright © 2011 Takeshi Yamamoto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) concept, in which Fuzzy c-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.