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Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
2011 (English)Conference paper (Refereed)Text
Abstract [en]

The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

Place, publisher, year, edition, pages
2011. Vol. 1371, 65-72 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-127945DOI: 10.1063/1.3596628OAI: oai:DiVA.org:liu-127945DiVA: diva2:928783
Conference
2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11). 11–13 October 2011,Toyama City, (Japan)
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2016-05-27

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Medical Image Processing

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ReferencesLink to record
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