A comparative study on TIBA imputation methods in FCMdd-based linear clustering with relational data

Authors:
Takeshi Yamamoto;Katsuhiro Honda;Akira Notsu;Hidetomo Ichihashi
Affiliations:
Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan
Venue:
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms: Theoretical Aspects and Applications to Fuzzy Systems
Year:
2011

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Abstract

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.