M-SOM: matricial self organizing map for sequence clustering and classification

This work presents approaches for sequence clustering and classification. These approaches use the self organizing map "SOM". The inputs of the map are modelled in order to take into account the information and the correlation of the patterns contained in the sequences. The first approaches represent the input of the map by a representative vector or by a covariance matrix in order to take into account the correlations between the sequence components. These approaches do not take into account the temporal order in the sequences (the dynamics). The other approaches introduce the dynamics in the covariance matrix. When covariance matrices represent sequences, the SOM is modified in order to take into account the fact that the inputs are matrices. The experimentations show that our approaches are better than some other temporal self organizing maps for user Web navigation classification.