NDLI: Extending external validity measures for determining the number of clusters

Content Provider	IEEE Xplore Digital Library
Author	Qinpei Zhao Mantao Xu Franti, P.
Copyright Year	2011
Description	Author affiliation: School of Electronics & Information, Shanghai Dian Ji University, Shanghai, China (Mantao Xu) \|\| School of Computing, University of Eastern Finland, Joensuu, Finland (Qinpei Zhao; Franti, P.)
Abstract	External validity measures in cluster analysis evaluate how well the clustering results match to a prior knowledge about the data. However, it is always intractable to get the prior knowledge in the practical problem of unsupervised learning, such as cluster analysis. In this paper, we extend the external validity measures for both hard and soft partitions by a resampling method, where no prior information is needed. To lighten the time burden caused by the resampling method, we incorporate two approaches into the proposed method: (i) extending external validity measures for soft partitions in a computational time of $O(M^{2}N);$ (ii) an efficient sub-sampling method with time complexity of O(N). The proposed method is then applied and reviewed in determining the number of clusters for the problem of unsupervised learning, cluster analysis. Experimental results has demonstrated the proposed method is very effective in solving the number of clusters.
Starting Page	931
Ending Page	936
File Size	389637
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121777
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Correlation image segmentation Clustering algorithms external cluster validity subsampling Educational institutions Time measurement clustering Partitioning algorithms Indexes
Content Type	Text
Resource Type	Article

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