NDLI: A new cluster validity index for data with merged clusters and different densities

Content Provider	IEEE Xplore Digital Library
Author	Lam, B.S.Y. Hong Yan
Copyright Year	2005
Description	Author affiliation: Dept. of Electron. Eng., City Univ. of Hong Kong, China (Lam, B.S.Y.; Hong Yan)
Abstract	Several cluster validity measures have been proposed for evaluating clustering results. However, existing methods may not work well for the following two kinds of data sets. The first one is that the data set contains cluster groups with different densities. The second one is that some of the cluster groups are closely positioned. In this paper, we introduce a new cluster validity index. In this method, we define the index as the ratio between the squared total length of the data eigen-axes and the between-cluster separation. Compared with existing cluster validity indices, the proposed index produces more accurate results and is able to handle the two kinds of data sets mentioned above.
Starting Page	798
Ending Page	803
File Size	4170895
Page Count	6
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571244
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Density measurement Clustering algorithms Partitioning algorithms Length measurement Electric variables measurement Unsupervised learning Clustering methods Testing Cost function Scattering Data Classification Clustering Cluster Validity Unsupervised Learning
Content Type	Text
Resource Type	Article

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