Paper Info
Title
Fuzzy c-Means Clustering of Incomplete Data Using Dimension-Wise Fuzzy Variances of Clusters.
Abstract
Clustering is an important technique for identifying groups of similar data objects within a data set. Since problems during the data collection and data preprocessing steps often lead to missing values in the data sets, there is a need for clustering methods that can deal with such imperfect data. Approaches proposed in the literature for adapting the fuzzy c-means algorithm to incomplete data work well on data sets with equally sized and shaped clusters. In this paper we present an approach for adapting the fuzzy c-means algorithm to incomplete data that uses the dimension-wise fuzzy variances of clusters for imputation of missing values. In experiments on incomplete real and synthetic data sets with differently sized and shaped clusters, we demonstrate the benefit over the basic approach in terms of the assignment of data objects to clusters and the cluster prototype computation.
Year
DOI
Venue
2016
10.1007/978-3-319-40596-4_58
Communications in Computer and Information Science
Keywords
Field
DocType
Clustering,Fuzzy c-means (FCM),Incomplete data,Missing values
Fuzzy clustering,Data mining,CURE data clustering algorithm,Fuzzy classification,Correlation clustering,Computer science,Data pre-processing,FLAME clustering,Missing data,Cluster analysis
Conference
Volume
ISSN
Citations 
610
1865-0929
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Ludmila Himmelspach1244.62
Stefan Conrad2168105.91