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Cluster merging based on weighted Mahalanobis distance with application in digital mammography

A new clustering algorithm that uses a weighted Mahalanobis distance as a distance metric to perform partitional clustering is proposed. The covariance matrices of the generated clusters are used to determine cluster similarity and closeness so that clusters which are similar in shape and close in M...

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Main Authors: Younis, K., Karim, M., Hardie, R., Loomis, J., Rogers, S., DeSimio, M.
Format: Conference or Workshop Item
Language:English
Published: IEEE 1998
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/8810/1/Cluster_merging_based_on_weighted_Mahalanobis_distance_with_application_in_digital_mammography.pdf
http://eprints.um.edu.my/8810/
http://www.scopus.com/inward/record.url?eid=2-s2.0-0032306128&partnerID=40&md5=abd392ca5b0b1c59a4f056f67ba795c1 http://ieeexplore.ieee.org/xpls/absall.jsp?arnumber=710194&tag=1
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spelling my.um.eprints.88102014-03-25T07:03:26Z http://eprints.um.edu.my/8810/ Cluster merging based on weighted Mahalanobis distance with application in digital mammography Younis, K. Karim, M. Hardie, R. Loomis, J. Rogers, S. DeSimio, M. TA Engineering (General). Civil engineering (General) A new clustering algorithm that uses a weighted Mahalanobis distance as a distance metric to perform partitional clustering is proposed. The covariance matrices of the generated clusters are used to determine cluster similarity and closeness so that clusters which are similar in shape and close in Mahalanobis distance can be merged together serving the ultimate goal of automatically determining the optimal number of classes present in the data. Properties of the new algorithm are presented by examining the clustering quality for codebooks designed with the proposed method and another common method that uses Euclidean distance. The new algorithm provides better results than the competing method on a variety of data sets. Application of this algorithm to the problem of detecting suspicious regions in a mammogram is discussed. IEEE 1998 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/8810/1/Cluster_merging_based_on_weighted_Mahalanobis_distance_with_application_in_digital_mammography.pdf Younis, K. and Karim, M. and Hardie, R. and Loomis, J. and Rogers, S. and DeSimio, M. (1998) Cluster merging based on weighted Mahalanobis distance with application in digital mammography. In: Proceedings of the 1998 IEEE National Aerospace and Electronics Conference, NAECON, 1998, Dayton, OH, USA. http://www.scopus.com/inward/record.url?eid=2-s2.0-0032306128&partnerID=40&md5=abd392ca5b0b1c59a4f056f67ba795c1 http://ieeexplore.ieee.org/xpls/absall.jsp?arnumber=710194&tag=1
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Younis, K.
Karim, M.
Hardie, R.
Loomis, J.
Rogers, S.
DeSimio, M.
Cluster merging based on weighted Mahalanobis distance with application in digital mammography
description A new clustering algorithm that uses a weighted Mahalanobis distance as a distance metric to perform partitional clustering is proposed. The covariance matrices of the generated clusters are used to determine cluster similarity and closeness so that clusters which are similar in shape and close in Mahalanobis distance can be merged together serving the ultimate goal of automatically determining the optimal number of classes present in the data. Properties of the new algorithm are presented by examining the clustering quality for codebooks designed with the proposed method and another common method that uses Euclidean distance. The new algorithm provides better results than the competing method on a variety of data sets. Application of this algorithm to the problem of detecting suspicious regions in a mammogram is discussed.
format Conference or Workshop Item
author Younis, K.
Karim, M.
Hardie, R.
Loomis, J.
Rogers, S.
DeSimio, M.
author_facet Younis, K.
Karim, M.
Hardie, R.
Loomis, J.
Rogers, S.
DeSimio, M.
author_sort Younis, K.
title Cluster merging based on weighted Mahalanobis distance with application in digital mammography
title_short Cluster merging based on weighted Mahalanobis distance with application in digital mammography
title_full Cluster merging based on weighted Mahalanobis distance with application in digital mammography
title_fullStr Cluster merging based on weighted Mahalanobis distance with application in digital mammography
title_full_unstemmed Cluster merging based on weighted Mahalanobis distance with application in digital mammography
title_sort cluster merging based on weighted mahalanobis distance with application in digital mammography
publisher IEEE
publishDate 1998
url http://eprints.um.edu.my/8810/1/Cluster_merging_based_on_weighted_Mahalanobis_distance_with_application_in_digital_mammography.pdf
http://eprints.um.edu.my/8810/
http://www.scopus.com/inward/record.url?eid=2-s2.0-0032306128&partnerID=40&md5=abd392ca5b0b1c59a4f056f67ba795c1 http://ieeexplore.ieee.org/xpls/absall.jsp?arnumber=710194&tag=1
_version_ 1643688393991782400
score 13.244368

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