Home           Contact us           FAQs           
    
     Journal Home     |     Aim & Scope    |    Author(s) Information      |     Editorial Board     |     MSP Download Statistics
    Abstract
2017 (Vol. 14, Issue: 8)
Research Article

A Refined Rough K-Means Clustering Algorithm based on Minimizing the Effect of Local Outlier Objects to Improve Overlapping Detection

Khaled Ali Othman, Md. Nasir Sulaiman, Norwati Mustapha and Nurfadhlina Mohd Sharef
Department of Computer Science, Faculty of Computer Science and IT Putra University of Malaysia, Selangor, Malaysia
 

DOI: 10.19026/rjaset.14.4952
Submitted Accepted Published
December 14, 2016 May 25, 2017 August 15, 2017

  How to Cite this Article:

Khaled Ali Othman, Md. Nasir Sulaiman, Norwati Mustapha and Nurfadhlina Mohd Sharef, 2017. A Refined Rough K-Means Clustering Algorithm based on Minimizing the Effect of Local Outlier Objects to Improve Overlapping Detection.  Research Journal of Applied Sciences, Engineering and Technology, 14(8): 281-290.

DOI: 10.19026/rjaset.14.4952

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.4952

Abstract:


In order to improve the quality of overlapping detection, Rough K-Means (RKM) was proposed as the first kind of rough clustering algorithm. It was found that this recent RKM algorithm known as π RKM is the most powerful and effective version in which there is an increase in the number of objects that are correctly clustered and a decrease in the number objects that are incorrectly clustered compared to the issues which the previous RKM had. However, there are challenges associated with the clustering process which uses RKM as a result of the difficulty in establishing a standard measure for reducing the effect of local outlier objects on a means function. Therefore, the RKM algorithm is refined in this study to address the problem. Through this study we contribute two components. Firstly, we intend to employ the use of Local Outlier Factor (LOF) technique for the discrimination of a number of objects as outliers and secondly, we propose to reduce the effect of local outliers on means function by using a weight. The result of the experiments which were performed through the use of synthetic and real life datasets prove that there is an improvement in the quality of overlapping detection when compared to recent versions.

Abstract PDF HTML
    References


    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

    Copyright

© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Current Information
   Sales & Services
Home  |  Contact us  |  About us  |  Privacy Policy
Copyright © 2015. MAXWELL Scientific Publication Corp., All rights reserved