M-SOM-ART: growing self organizing map for sequences clustering and classification
Farida Zehraoui, Younès Bennani · European Conference on Artificial Intelligence · 2004
This paper presents a new growing neural network for sequences clustering and classification. This network is a self organizing map (SOM), which has the properties of stability and plasticity. The stability concerns the preservation of previously learned knowledge and the plasticity concerns the adaptation to any change in the input environment. These properties are obtained using Adaptive Resonance Theory. In order to take into account the temporal information (the dynamics) and the correlation of the patterns contained in the sequences, the inputs of the map are modelled using their associated dynamic covariance matrices. This new model is inspired from the field of speaker recognition. We have modified a covariance matrix in order to represent a temporal order in the sequence. The experimentations show that our approach is better than some other temporal self organizing map for user's Web navigation classification.