NDLI: A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters

Content Provider	IEEE Xplore Digital Library
Author	Muhlenbach, F. Lallich, S.
Copyright Year	2009
Abstract	Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-based clustering method called GBC which detects automatically the best number of clusters, without requiring any other parameter. In this method based on regions of influence, a graph is constructed and the edges of the graph having the higher values are cut according to a hierarchical divisive procedure. An index is calculated from the size average of the cut edges which self-detects the more appropriate number of clusters. The results of GBC for 3 quality indices (Dunn, Silhouette and Davies-Bouldin) are compared with those of K-Means, Ward's hierarchical clustering method and DBSCAN on 8 benchmarks. The experiments show the good performance of GBC in the case of well separated clusters, even if the data are unbalanced, non-convex or with presence of outliers, whatever the shape of the clusters.
Starting Page	884
Ending Page	889
File Size	214377
Page Count	6
File Format	PDF
ISBN	9781424452422
ISSN	15504786
DOI	10.1109/ICDM.2009.133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Clustering methods Partitioning algorithms Iterative algorithms Data mining Shape Tree graphs Machine learning Image edge detection Bioinformatics neighborhood graph clustering
Content Type	Text
Resource Type	Article

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