Incremental Possibilistic Approach for Online Clustering and Classification

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. Keywords-- Classification, clustering, Sequential learning, Fuzzy Pattern Matching.

• Type: Journal
• Year: 2009
• Where: EUSFLAT
• Authors: Moamar Sayed Mouchaweh, Bernard Riera
• Keywords: EUSFLAT 2009, Fuzzy Logic, Fuzzy Pattern Matching, Supervised Classification, Supervised Classification Method