Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data

Fuzzy C-Means (FCM) is one of the most well-known clustering algorithms, nevertheless its performance has been limited by the utilization of Euclidean as its distance metric.Even though there exist studies that applied FCM with other distance metrics such as Manhattan, Minkowski and Chebyshev, its p...

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Main Authors: Mousa, Aseel, Yusof, Yuhanis
Format: Article
Language:English
Published: Medwell Publishing 2018
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Online Access:http://repo.uum.edu.my/24433/1/JEAS%2013%202%202018%20353-360.pdf
http://repo.uum.edu.my/24433/
https://www.medwelljournals.com/abstract/?doi=jeasci.2018.353.360
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spelling my.uum.repo.244332018-07-23T01:20:25Z http://repo.uum.edu.my/24433/ Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data Mousa, Aseel Yusof, Yuhanis QA75 Electronic computers. Computer science Fuzzy C-Means (FCM) is one of the most well-known clustering algorithms, nevertheless its performance has been limited by the utilization of Euclidean as its distance metric.Even though there exist studies that applied FCM with other distance metrics such as Manhattan, Minkowski and Chebyshev, its performance can still be argued particularly on multi-label data.Various applications rely on data points that can be grouped into more than one class and this includes protein function classification and image annotation.This study proposes the employment of FCM that is implement using an improved Chebyshev distance metric.The proposed work eliminates correlation in data points and improve performance of clustering.The results show that the proposed FCM improves the performance of clustering as it produces minimum objective function value and with less iteration count. Such a result indicates that FCM with improved distance metric contributes in producing better clusters. Medwell Publishing 2018 Article PeerReviewed application/pdf en http://repo.uum.edu.my/24433/1/JEAS%2013%202%202018%20353-360.pdf Mousa, Aseel and Yusof, Yuhanis (2018) Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data. Journal of Engineering and Applied Sciences, 13 (2). pp. 353-360. ISSN 1816-949X https://www.medwelljournals.com/abstract/?doi=jeasci.2018.353.360
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mousa, Aseel
Yusof, Yuhanis
Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
description Fuzzy C-Means (FCM) is one of the most well-known clustering algorithms, nevertheless its performance has been limited by the utilization of Euclidean as its distance metric.Even though there exist studies that applied FCM with other distance metrics such as Manhattan, Minkowski and Chebyshev, its performance can still be argued particularly on multi-label data.Various applications rely on data points that can be grouped into more than one class and this includes protein function classification and image annotation.This study proposes the employment of FCM that is implement using an improved Chebyshev distance metric.The proposed work eliminates correlation in data points and improve performance of clustering.The results show that the proposed FCM improves the performance of clustering as it produces minimum objective function value and with less iteration count. Such a result indicates that FCM with improved distance metric contributes in producing better clusters.
format Article
author Mousa, Aseel
Yusof, Yuhanis
author_facet Mousa, Aseel
Yusof, Yuhanis
author_sort Mousa, Aseel
title Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
title_short Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
title_full Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
title_fullStr Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
title_full_unstemmed Fuzzy C-Means with Improved Chebyshev Distance for Multi-Labelled Data
title_sort fuzzy c-means with improved chebyshev distance for multi-labelled data
publisher Medwell Publishing
publishDate 2018
url http://repo.uum.edu.my/24433/1/JEAS%2013%202%202018%20353-360.pdf
http://repo.uum.edu.my/24433/
https://www.medwelljournals.com/abstract/?doi=jeasci.2018.353.360
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