Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

Algorithms

Joonas Hämäläinen¹  Tommi Kärkkäinen¹  Susanne Jauhiainen¹ 

[1] These authors contributed equally to this work.;

关键词: prototype-based clustering;    clustering validation index;    robust statistics;   

DOI  :  10.3390/a10030105

来源: DOAJ

【 摘 要 】

Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on the behavior of different variants of clustering algorithms will be given.

【 授权许可】

Unknown