NDLI: A Visual Approach for External Cluster Validation

Content Provider	IEEE Xplore Digital Library
Author	Ke-Bing Zhang Orgun, M.A. Kang Zhang
Copyright Year	2007
Description	Author affiliation: Dept. of Comput., Macquarie Univ., Sydney, NSW (Ke-Bing Zhang; Orgun, M.A.)
Abstract	Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (hypothesis oriented verification and validation by visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions
Starting Page	576
Ending Page	582
File Size	339418
Page Count	7
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional systems Clustering algorithms Data visualization Sampling methods Extraterrestrial measurements Partitioning algorithms Data mining Visual databases Application software Computational intelligence
Content Type	Text
Resource Type	Article

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