A new approach for evaluating internal cluster validation indices
Machine Learning
2023-08-09 v1 Machine Learning
Abstract
A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. These internal validation indices can be evaluated by applying them to classifications of datasets with a known cluster structure. Evaluation approaches differ in how they use the information on the ground-truth classification. This paper reviews these approaches, considering their advantages and disadvantages, and then suggests a new approach.
Cite
@article{arxiv.2308.03894,
title = {A new approach for evaluating internal cluster validation indices},
author = {Zoltán Botta-Dukát},
journal= {arXiv preprint arXiv:2308.03894},
year = {2023}
}