Paper Info
Title
Incremental Possibilistic Approach For Online Clustering And Classification
Abstract
In this paper, we propose to develop the supervised classification method Fuzzy Pattern Matching to be in addition a non supervised one. The goal is to monitor dynamic systems with a limited prior knowledge about their functioning. The detection of the occurrence of new states as well as the reinforcement of the estimation of their membership functions are performed online thanks to the combination of supervised and non supervised classification modes. No information in advance about the shape of classes or their number is required to achieve this detection and estimation reinforcement.
Year
Venue
Keywords
2009
PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE
Classification, clustering, Sequential learning, Fuzzy Pattern Matching
Field
DocType
Citations 
Fuzzy classification,Pattern recognition,Artificial intelligence,Fuzzy pattern matching,Cluster analysis,Reinforcement,Dynamical system,Machine learning,Mathematics
Conference
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Moamar Sayed Mouchaweh119118.27
Bernard Riera2248.31