A new validity index for crisp clusters

In this paper, a new cluster validity index which can be considered as a measure of the accuracy of the partitioning of data sets is proposed. The new index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a...

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Bibliographic Details
Published inPattern analysis and applications : PAA Vol. 20; no. 3; pp. 687 - 700
Main Author Starczewski, Artur
Format Journal Article
LanguageEnglish
Published London Springer London 01.08.2017
Springer Nature B.V
Subjects
Algorithms
Clustering
Clusters
Computer Science
Datasets
Pattern Recognition
Theoretical Advances
Clustering
Validity index
Unsupervised classification
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Summary:In this paper, a new cluster validity index which can be considered as a measure of the accuracy of the partitioning of data sets is proposed. The new index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a clustering process. The maximum value of this index identifies the best clustering scheme. Three popular algorithms have been applied as underlying clustering techniques, namely complete-linkage, expectation maximization and K -means algorithms. The performance of the new index is demonstrated for several artificial and real-life data sets. Moreover, this new index has been compared with other well-known indices, i.e., Dunn, Davies-Bouldin, PBM and Silhouette indices, taking into account the number of clusters in a data set as the comparison criterion. The results prove superiority of the new index as compared to the above-mentioned indices.
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ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-015-0525-8