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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2021

19.01.2021

Estimating the Optimal Number of Clusters Via Internal Validity Index

verfasst von: Shibing Zhou, Fei Liu, Wei Song

Erschienen in: Neural Processing Letters | Ausgabe 2/2021

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Abstract

Estimating the optimal number of clusters (NC) is pivotal in cluster analysis. From the viewpoint of sample geometry, a novel internal clustering validity index, which is termed the between-within cluster (BWC) index, is designed in this paper. Moreover, a method is proposed to estimate the optimal NC. The BWC index improves the well-known Silhouette index. BWC validates the clustering results from a certain clustering algorithm (e.g., affinity propagation or hierarchical) and estimates the optimal NC for many kinds of data sets, including synthetic data sets, benchmark data sets, UCI data sets, gene expression data sets, and images. Theoretical analysis and experimental studies demonstrate the effectiveness and high efficiency of the new index and method.
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Metadaten
Titel
Estimating the Optimal Number of Clusters Via Internal Validity Index
verfasst von
Shibing Zhou
Fei Liu
Wei Song
Publikationsdatum
19.01.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10427-8

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