Cluster analysis of antigenic profiles of tumors: selection of number of clusters using Akaike's information criterion.
A basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike's information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.
Koziol
J A
JA
Department of Molecular and Experimental Medicine, Research Institute of Scripps Clinic, La Jolla, CA.
D000465:Algorithms; D000951:Antigens, Neoplasm / Q000032:analysis*; D000954:Antigens, Surface / Q000032:analysis; D016000:Cluster Analysis; D006801:Humans; D007150:Immunohistochemistry; D009369:Neoplasms / Q000145:classification* / Q000276:immunology
D000951:Antigens, Neoplasm; D000954:Antigens, Surface