Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer
2011 (English)Conference paper (Refereed)Text
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.
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-127945DOI: 10.1063/1.3596628OAI: oai:DiVA.org:liu-127945DiVA: diva2:928783
2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11). 11–13 October 2011,Toyama City, (Japan)