http://dx.doi.org/10.15446/rce.v37n1.44359

¹Universidad de Sevilla, Facultad de Ciencias Económicas y Empresariales, Departamento de Economía Aplicada I, Sevilla, Spain. Senior lecturer. Email: luisgon@us.es
²Universidad de Sevilla, Facultad de Ciencias Económicas y Empresariales, Departamento de Economía Aplicada I, Sevilla, Spain. Senior lecturer. Email: gavi@us.es
³Universidad de Sevilla, Facultad de Ciencias Económicas y Empresariales, Departamento de Economía Aplicada I, Sevilla, Spain. Senior lecturer. Email: velasco@us.es

Based on an interval distance, three functions are given in order to quantify similarities between one-dimensional data sets by using first-order statistics. The Glass Identification Database is used to illustrate how to analyse a data set prior to its classification and/or to exclude dimensions. Furthermore, a non-parametric hypothesis test is designed to show how these similarity measures, based on random samples from two populations, can be used to decide whether these populations are identical. Two comparative analyses are also carried out with a parametric test and a non-parametric test. This new non-parametric test performs reasonably well in comparison with classic tests.

Key words: Data mining, Interval distance, Kernel methods, Non-parametric tests.

Basadas en una distancia intervalar, se dan tres funciones para cuantificar similaridades entre conjuntos de datos unidimensionales mediante el uso de estadísticos de primer orden. Se usa la base de datos Glass Identification para ilustrar cómo esas medidas de similaridad se pueden usar para analizar un conjunto de datos antes de su clasificación y/o para excluir dimensiones. Además, se diseña un test de hipótesis no parámetrico para mostrar cómo similaridad, basadas en muestras aleatorias de dos poblaciones, se pueden usar para decidir si esas poblaciones son idénticas. También se realizan dos análisis comparativos con un test paramétrico y un test no paramétrico. Este nuevo test se comporta razonablemente bien en comparación con test clásicos.

Palabras clave: distancia entre intervalos, métodos del núcleo, minería de datos, tests no paramétricos.

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