An improved Chebyshev distance metric for clustering medical images

A metric or distance function is a function which defines a distance between elements of a set. In clustering, measuring the similarity between objects has become an important issue. In practice, there are various similarity measures used and this includes the Euclidean, Manhattan and Minkowski.In t...

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Main Authors: Mousa, Aseel, Yusof, Yuhanis
格式: Conference or Workshop Item
出版: IP Publishing LLC 2015
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在線閱讀:http://repo.uum.edu.my/20648/
http://doi.org/10.1063/1.4937070
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spelling my.uum.repo.206482017-01-18T03:48:08Z http://repo.uum.edu.my/20648/ An improved Chebyshev distance metric for clustering medical images Mousa, Aseel Yusof, Yuhanis QA75 Electronic computers. Computer science A metric or distance function is a function which defines a distance between elements of a set. In clustering, measuring the similarity between objects has become an important issue. In practice, there are various similarity measures used and this includes the Euclidean, Manhattan and Minkowski.In this paper, an improved Chebyshev similarity measure is introduced to replace existing metrics (such as Euclidean and standard Chebyshev) in clustering analysis.The proposed measure is later realized in analyzing blood cancer images. Results demonstrate that the proposed measure produces the smallest objective function value and converge at the lowest number of iteration.Hence, it can be concluded that the proposed distance metric contribute in producing better clusters. IP Publishing LLC 2015 Conference or Workshop Item PeerReviewed Mousa, Aseel and Yusof, Yuhanis (2015) An improved Chebyshev distance metric for clustering medical images. In: 2nd Innovation and Analytics Conference & Exhibition (IACE 2015), 29 September –1 October 2015, TH Hotel, Alor Setar, Kedah, Malaysia. http://doi.org/10.1063/1.4937070 doi:10.1063/1.4937070
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/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mousa, Aseel
Yusof, Yuhanis
An improved Chebyshev distance metric for clustering medical images
description A metric or distance function is a function which defines a distance between elements of a set. In clustering, measuring the similarity between objects has become an important issue. In practice, there are various similarity measures used and this includes the Euclidean, Manhattan and Minkowski.In this paper, an improved Chebyshev similarity measure is introduced to replace existing metrics (such as Euclidean and standard Chebyshev) in clustering analysis.The proposed measure is later realized in analyzing blood cancer images. Results demonstrate that the proposed measure produces the smallest objective function value and converge at the lowest number of iteration.Hence, it can be concluded that the proposed distance metric contribute in producing better clusters.
format Conference or Workshop Item
author Mousa, Aseel
Yusof, Yuhanis
author_facet Mousa, Aseel
Yusof, Yuhanis
author_sort Mousa, Aseel
title An improved Chebyshev distance metric for clustering medical images
title_short An improved Chebyshev distance metric for clustering medical images
title_full An improved Chebyshev distance metric for clustering medical images
title_fullStr An improved Chebyshev distance metric for clustering medical images
title_full_unstemmed An improved Chebyshev distance metric for clustering medical images
title_sort improved chebyshev distance metric for clustering medical images
publisher IP Publishing LLC
publishDate 2015
url http://repo.uum.edu.my/20648/
http://doi.org/10.1063/1.4937070
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