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A classification approach based on variable precision rough sets and cluster validity index function

Hongkang Lin (Department of Computer and Information Engineering, Ningde Normal University, Ningde, China)

Engineering Computations

ISSN: 0264-4401

Article publication date: 28 October 2014

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Abstract

Purpose

The clustering/classification method proposed in this study, designated as the PFV-index method, provides the means to solve the following problems for a data set characterized by imprecision and uncertainty: first, discretizing the continuous values of all the individual attributes within a data set; second, evaluating the optimality of the discretization results; third, determining the optimal number of clusters per attribute; and fourth, improving the classification accuracy (CA) of data sets characterized by uncertainty. The paper aims to discuss these issues.

Design/methodology/approach

The proposed method for the solution of the clustering/classifying problem, designated as PFV-index method, combines a particle swarm optimization algorithm, fuzzy C-means method, variable precision rough sets theory, and a new cluster validity index function.

Findings

This method could cluster the values of the individual attributes within the data set and achieves both the optimal number of clusters and the optimal CA.

Originality/value

The validity of the proposed approach is investigated by comparing the classification results obtained for UCI data sets with those obtained by supervised classification BPNN, decision-tree methods.

Keywords

Citation

Lin, H. (2014), "A classification approach based on variable precision rough sets and cluster validity index function", Engineering Computations, Vol. 31 No. 8, pp. 1778-1789. https://doi.org/10.1108/EC-11-2012-0297

Publisher

:

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited