Paper Info
Title
A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm.
Abstract
In this paper, we present a new possibilistic multivariate fuzzy c-means (PMFCM) clustering algorithm. PMFCM is a combination of multivariate fuzzy c-means (MFCM) and possibilistic fuzzy c-means (PFCM) that produces membership degrees of data objects to each cluster according to each feature and typicality values of data objects to each cluster. In this way, PMFCM produces a multivariate partitioning of a data set detecting clusters with unevenly distributed data over different features. It also reduces the influence of noise and outliers to computation of cluster centers.
Year
DOI
Venue
2016
10.1007/978-3-319-45856-4_24
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Fuzzy clustering,c-Means models,Possibilistic clustering,Multivariate memberships
Data mining,Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Computer science,Multivariate statistics,Fuzzy logic,Outlier,Cluster analysis,Computation
Conference
Volume
ISSN
Citations 
9858
0302-9743
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Ludmila Himmelspach1244.62
Stefan Conrad2168105.91