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.
High-content screening, fuzzy c-means clustering, cluster validity, joint-feature-clustering.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-125035DOI: 10.1063/1.3596628ISBN: 978-0-7354-0931OAI: oai:DiVA.org:liu-125035DiVA: diva2:902756
2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11. 11–13 October 2011Toyama City, (Japan)