Skip to Main content Skip to Navigation
Conference papers

Kernel Hierarchical Agglomerative Clustering - Comparison of Different Gap Statistics to Estimate the Number of Clusters

Abstract : Clustering algorithms, as unsupervised analysis tools, are useful for exploring data structure and have owned great success in many disciplines. For most of the clustering algorithms like k-means, determining the number of the clusters is a crucial step and is one of the most difficult problems. Hierarchical Agglomerative Clustering (HAC) has the advantage of giving a data representation by the dendrogram that allows clustering by cutting the dendrogram at some optimal level. In the past years and within the context of HAC, efficient statistics have been proposed to estimate the number of clusters and the Gap Statistic by Tibshirani has shown interesting performances. In this paper, we propose some new Gap Statistics to further improve the determination of the number of clusters. Our works focus on the kernelized version of the widely-used Hierarchical Clustering Algorithm.
Document type :
Conference papers
Complete list of metadatas

https://hal-utt.archives-ouvertes.fr/hal-02861450
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, June 9, 2020 - 7:57:45 AM
Last modification on : Wednesday, June 10, 2020 - 5:35:03 AM

Links full text

Identifiers

Collections

ROSAS | UTT | CNRS

Citation

Na Li, Nicolas Lefèbvre, Régis Lengellé. Kernel Hierarchical Agglomerative Clustering - Comparison of Different Gap Statistics to Estimate the Number of Clusters. International Conference on Pattern Recognition Applications and Methods, Mar 2014, Angers, France. pp.255-262, ⟨10.5220/0004828202550262⟩. ⟨hal-02861450⟩

Share

Metrics

Record views

14