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-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-
