title: A knowledge-driven approach to cluster validity assessment.
abstract: This paper presents an approach to assessing cluster validity based on similarity knowledge extracted from the Gene Ontology.
  
  
  BACKGROUND
  The program is freely available for non-profit use on request from the authors.
authors.affiliation: Department of Computer Science, Trinity College Dublin, Dublin 2, Ireland. nadia.bolshakova@cs.tcd.ie
authors.forename: Nadia
authors.initials: N
authors.lastname: Bolshakova
authors.forename: Francisco
authors.initials: F
authors.lastname: Azuaje
authors.forename: Pádraig
authors.initials: P
authors.lastname: Cunningham
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003628:Database Management Systems; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D012984:Software; D014584:User-Computer Interface
chemical_list: D029701:Saccharomyces cerevisiae Proteins