Paper Info
Title
A comparative study on TIBA imputation methods in FCMdd-based linear clustering with relational data
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.
Year
DOI
Venue
2011
10.1155/2011/265170
Adv. Fuzzy Systems
Keywords
Field
DocType
linear cluster,fuzzy c-means,fcmdd-based linear clustering,tiba imputation method,fuzzy c-medoids,relational fuzzy clustering,comparative study,relational data,representative medoids,incomplete data,intrinsic cluster structure,linear fuzzy clustering model,fcmdd-type linear clustering model
Data mining,Fuzzy clustering,Relational database,Artificial intelligence,Missing data,Cluster analysis,Medoid,Pattern recognition,Iterative method,Fuzzy logic,Imputation (statistics),Machine learning,Mathematics
Journal
Volume
Citations 
PageRank 
2011,
0
0.34
References 
Authors
11
4
Name
Order
Citations
PageRank
Takeshi Yamamoto161.87
Katsuhiro Honda228963.11
Akira Notsu314642.93
Hidetomo Ichihashi437072.85