An unsupervised technique to discretize numerical values by fuzzy partitions

Issue title: selected papers from Intelligent Environments 2017

Guest editors: Jason J. Jung and Paulo Novais

Article type: Research Article

Authors: Bueno-Crespo, Andrés | Martínez-España, Raquel^{; *} | Timón, Isabel | Soto, Jesús

Affiliations: Dept. of Computer Engineering, Catholic University of Murcia, Murcia, Spain. E-mails: abueno@ucam.edu, rmartinez@ucam.edu, imtimon@ucam.edu, jsoto@ucam.edu

Correspondence: [*] Corresponding author. E-mail: rmartinez@ucam.edu.

Abstract: The numerical value discretization is a process that is performed in the data preprocessing phase of intelligent data analysis. Preprocessing phase is very relevant because the quality of the models obtained in data mining step depends on this phase. Value discretization is an important task in data preprocessing because not all data mining techniques can handle continuous values. In this paper an unsupervised technique to discretize continuous data values using fuzzy partitions is proposed. Specifically a clustering technique that gets fuzzy partitions is presented. In addition, to evaluate the behavior of the proposed technique a series of experiments have been proposed using a Extreme Learning Machine classifier and a committee of Extreme Learning Machine. Beside comparing with the K-means discretization technique. These experiments have been validated statistically obtaining the best results the approach proposed.

Keywords: Discretization, fuzzy clustering, membership function, extreme learning machine, data mining

DOI: 10.3233/AIS-180488

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 10, no. 3, pp. 289-300, 2018