A knowledge-driven approach to cluster validity assessment.
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.
Bolshakova
Nadia
N
Department of Computer Science, Trinity College Dublin, Dublin 2, Ireland. nadia.bolshakova@cs.tcd.ie
Azuaje
Francisco
F
Cunningham
Pádraig
P
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
D029701:Saccharomyces cerevisiae Proteins