Title :
Fuzzy cluster analysis of classified data
Author_Institution :
Dept. of Knowledge Process. & Language Eng., Otto-von-Guericke-Univ. Magdeburg, Germany
Abstract :
Fuzzy cluster analysis is a method for unsupervised clustering. However sometimes class information is available for the given dataset, i.e., only the number of clusters per class is unknown. In this paper it is discussed how class information can be exploited. Some common approaches are reviewed and a new approach is suggested, which integrates class information into fuzzy cluster analysis
Keywords :
fuzzy logic; image classification; pattern clustering; classified data; fuzzy cluster analysis; unsupervised clustering; Clustering algorithms; Data analysis; Humans; Information analysis; Knowledge engineering; Prototypes;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943759
