title: Obtaining better quality final clustering by merging a collection of clusterings.
abstract: 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
authors.affiliation: Department of Computer Engineering, Bahcesehir University, Ciragan Caddesi 34353 Besiktas, Istanbul, Turkey. selim.mimaroglu@bahcesehir.edu.tr
authors.forename: Selim
authors.initials: S
authors.lastname: Mimaroglu
authors.forename: Ertunc
authors.initials: E
authors.lastname: Erdil
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D011506:Proteins / Q000032:analysis
chemical_list: D011506:Proteins