NDLI: A New Cluster Validity for Data Clustering

Content Provider	Springer Nature Link
Author	Yang, Xulei Cao, Aize Song, Qing
Copyright Year	2006
Abstract	Cluster validity has been widely used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity, which is called the Vapnik–Chervonenkis-bound (VB) index, for data clustering. It is estimated based on the structural risk minimization (SRM) principle, which optimizes the bound simultaneously over both the distortion function (empirical risk) and the VC-dimension (model complexity). The smallest bound of the guaranteed risk achieved on some appropriate cluster number validates the best description of the data structure. We use the deterministic annealing (DA) algorithm as the underlying clustering technique to produce the partitions. Five numerical examples and two real data sets are used to illustrate the use of VB as a validity index. Its effectiveness is compared to several popular cluster-validity indexes. The results of comparative study show that the proposed VB index has high ability in producing a good cluster number estimate and in addition, it provides a new approach for cluster validity from the view of statistical learning theory.
Starting Page	325
Ending Page	344
Page Count	20
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	23
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2006-06-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	cluster validity data clustering deterministic annealing structural risk minimization Vapnik–Chervonenkis-bound Operations Research/Decision Theory Electronic and Computer Engineering Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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