NDLI: A new inter cluster validation method for unsupervised clustering techniques

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A new inter cluster validation method for unsupervised clustering techniques

Content Provider	IEEE Xplore Digital Library
Author	Krishnamoorthy, R. Kumar, S.S.
Copyright Year	2013
Description	Author affiliation: Dept. of CSE, Don Bosco Inst. of Technol., Bangalore, India (Kumar, S.S.) \|\| Dept. of CSE, Anna Univ. :Chennai, Tiruchirappalli, India (Krishnamoorthy, R.)
Abstract	In this paper, a new cluster validation technique called Inter Cluster Validation (ICV) is presented. The proposed technique is aimed to measure the inter cluster similarity and inter cluster dissimilarity over the each individual cluster with other clusters in the resulting cluster of the unsupervised clustering techniques. The proposed ICV technique consists of two measures which are Inter Cluster Similarity (ICS) and Inter Cluster Dissimilarity (ICD). The first measure (ICS), is aimed to measure the inter cluster similarity over the each individual cluster with other cluster in resulting cluster and, it also calculate the overall resulting cluster similarity. The second measure (ICD), is evaluate the inter cluster dissimilarity over the each individual with other clusters in the resulting cluster. The proposed ICV technique is test over the resulting cluster of the four unsupervised clustering techniques are K-Means, CURE, OAC and LIAC. Experimental results show that the ICV technique is simple and better suitable for evaluating the inter cluster similarity and inter cluster dissimilarity over the each individual cluster belongs to the resulting cluster of the unsupervised clustering techniques such as K-Means, CURE, OAC and LIAC.
Starting Page	1
Ending Page	5
File Size	2831994
Page Count	5
File Format	PDF
e-ISBN	9781479962549
DOI	10.1109/ICCCV.2013.6906741
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-20
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimized Agglomerative Clustering Measurement uncertainty Limited Iteration Agglomerative Clustering (LIAC) Clustering algorithms Size measurement Inter Cluster Similarity (ICS) Pattern recognition Noise measurement Inter Cluster Dissimilarity (ICD) Equations Inter Cluster Validation (ICV) IEEE transactions
Content Type	Text
Resource Type	Article

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