Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Botta-Dukát, Zoltán
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4385714536