Paper Info
Title
Minimum sum-squared residue for fuzzy co-clustering
Abstract
Clustering is often seen as a more practical but very challenging answer to the task of categorizing objects. Minimum Sum-squared Residue for Fuzzy Co-Clustering (MSR-FCC) is proposed to address two issues faced by many existing clustering algorithms, namely the high-dimensionality and the inherent fuzziness found in most real-world data. MSR-FCC is able to simultaneously cluster data and features using fuzzy techniques. It suggests a new partitioning fuzzy co-clustering algorithm based on the mean squared residue approach. Besides handling overlap clusters, MSR-FCC offers the flexibility that allows the number of data clusters to be different from the number of feature clusters, which reflects the distribution characteristic inherited in real-world data. In this paper, mathematical formulation of MSR-FCC is derived and explained. Experiments were conducted on standard datasets to demonstrate that the proposed algorithm is able to cluster high-dimensional data with overlaps feasibly and at the same time, it provides a new and promising mechanism for improving the interpretability of the co-clusters through the fuzzy membership function.
Year
DOI
Venue
2006
10.3233/IDA-2006-10304
Intell. Data Anal.
Keywords
Field
DocType
real-world data,cluster data,data cluster,existing clustering algorithm,partitioning fuzzy co-clustering algorithm,feature cluster,fuzzy membership function,fuzzy technique,cluster high-dimensional data,minimum sum-squared residue,proposed algorithm,clustering,fuzzy set
Data mining,Fuzzy clustering,CURE data clustering algorithm,Fuzzy classification,Computer science,Fuzzy set operations,FLAME clustering,Artificial intelligence,Fuzzy number,Cluster analysis,Correlation clustering,Pattern recognition,Machine learning
Journal
Volume
Issue
ISSN
10
3
1088-467X
Citations 
PageRank 
References 
3
0.43
18
Authors
2
Name
Order
Citations
PageRank
William-Chandra Tjhi115610.09
Lihui Chen238027.30