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

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.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Conference code: 49281 Cited By (since 1996):4 Export Date: 16 December 2013 Source: Scopus CODEN: NASEA Language of Original Document: English Correspondence Address: Younis, Khaled; Univ of Dayton, Dayton, United States Sponsors: IEEE
Uncontrolled Keywords: Data structures, Fuzzy sets, Iterative methods, Matrix algebra, Membership functions, Optimization, Vectors, Digital mammography, Generalized Lloyd algorithm, Algorithms
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: Mr Jenal S
Date Deposited: 25 Mar 2014 07:03
Last Modified: 25 Mar 2014 07:03
URI: http://eprints.um.edu.my/id/eprint/8810

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