Semi-Supervised Fuzzy Clustering with Feature Discrimination.
Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.
College of Mechanical & Electronic Engineering, Northwest A&F University, Shaanxi, 712100, P.R. China; College of Information Engineering, Shaanxi Polytechnic Institute, Shaanxi, 712000, P.R. China.
Longlong
L
Li
IMA group, School of Computer Science, University of Nottingham, Nottingham, NG81BB, United Kingdom.
Jonathan M
JM
Garibaldi
College of Mechanical & Electronic Engineering, Northwest A&F University, Shaanxi, 712100, P.R. China.
Dongjian
D
He
College of Information Engineering, Northwest A&F University, Shaanxi, 712100, P.R. China.
Meili
M
Wang
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D017143:Fuzzy Logic; D008962:Models, Theoretical