Obtaining better quality final clustering by merging a collection of clusterings.
BACKGROUND
Clustering methods including k-means, SOM, UPGMA, DAA, CLICK, GENECLUSTER, CAST, DHC, PMETIS and KMETIS have been widely used in biological studies for gene expression, protein localization, sequence recognition and more. All these clustering methods have some benefits and drawbacks. We propose a novel graph-based clustering software called COMUSA for combining the benefits of a collection of clusterings into a final clustering having better overall quality.


RESULTS
COMUSA implementation is compared with PMETIS, KMETIS and k-means. Experimental results on artificial, real and biological datasets demonstrate the effectiveness of our method. COMUSA produces very good quality clusters in a short amount of time.


BACKGROUND
http://www.cs.umb.edu/∼smimarog/comusa


BACKGROUND
selim.mimaroglu@bahcesehir.edu.tr
Mimaroglu
Selim
S
Department of Computer Engineering, Bahcesehir University, Ciragan Caddesi 34353 Besiktas, Istanbul, Turkey. selim.mimaroglu@bahcesehir.edu.tr
Erdil
Ertunc
E
D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D011506:Proteins / Q000032:analysis
D011506:Proteins