Title
Study Of Overlapping Clustering Algorithms Based On Kmeans Through Fbcubed Metric
Abstract
In this paper we present a study of the overlapping clustering algorithms OKM, WOKM and OKMED, which are extensions to the overlapping case of the well known Kmeans algorithm proposed for building partitions. Different to other previously reported comparisons, in our study we compare these algorithms using the external evaluation metric FBcubed which takes into account the overlapping among clusters and we contrast our results against those obtained by F-measure, a metric that does not take into account the overlapping among clusters and that has been previously used in another reported comparison.
Year
DOI
Venue
2014
10.1007/978-3-319-07491-7_12
PATTERN RECOGNITION, MCPR 2014
Keywords
Field
DocType
Clustering, Overlapping Clustering, Clustering Validation
k-means clustering,Cluster (physics),Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
8495
0302-9743
0
PageRank 
References 
Authors
0.34
13
5