A classification approach based on variable precision rough sets and cluster validity index function. Issue 8 (28th October 2014)

• Record Type: Journal Article
• Authors: Meen, Teen-Hang; D. Prior, Steven; Donald Kin-Tak Lam, Artde; Lin, Hongkang
• Is Part Of: Engineering computations. Volume 31:Issue 8(2014)
• Page Start: 1778
• Page End: 1789
• Publication Date: 2014-10-28
• Subjects: Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-11-2012-0297
• Languages: English
• ISSNs: 0264-4401

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – 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.</p> </sec> </abstract>