External clustering validity index based on chi-squared statistical test
| Autor: | Luna Romera, José María, Martínez Ballesteros, María del Mar, García Gutiérrez, Jorge, Riquelme Santos, José Cristóbal |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC-254: Data Science and Big Data Lab, Ministerio de Economía y Competitividad (MINECO). España |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | Clustering is one of the most commonly used techniques in data mining. Its main goal is to group objects into clusters so that each group contains objects that are more similar to each other than to objects in other clusters. The evaluation of a clustering solution is a task carried out through the application of validity indices. These indices measure the quality of the solution and can be classified as either internal that calculate the quality of the solution through the data of the clusters, or as external indices that measure the quality by means of external information such as the class. Generally, indices from the literature determine their optimal result through graphical representation, whose results could be imprecisely interpreted. The aim of this paper is to present a new external validity index based on the chi-squared statistical test named Chi Index, which presents accurate results that require no further interpretation. Chi Index was analyzed using the clustering results of 3 clustering methods in 47 public datasets. Results indicate a better hit rate and a lower percentage of error against 15 external validity indices from the literature. Ministerio de Economía y Competitividad TIN2014-55894-C2-R Ministerio de Economía y Competitividad TIN2017-88209-C2-2-R |
| Databáze: | OpenAIRE |
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