Document Type
Conference Item
Publication Date
1-1-1998
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.
Keywords
Data structures, Fuzzy sets, Iterative methods, Matrix algebra, Membership functions, Optimization, Vectors, Digital mammography, Generalized Lloyd algorithm, Algorithms
Publisher
IEEE
Event Title
Proceedings of the 1998 IEEE National Aerospace and Electronics Conference, NAECON
Event Location
Dayton, OH, USA
Event Dates
1998
Event Type
conference
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