Temporal neural networks such as Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM) are popular for their incremental and explicit learning abilities. However, for sub-sequence clustering TKM and RSOM may generate many fragments whose classification membership is hard to decide. Besides they have stability issues in multivariate time series processing because they model the historical neuron activities on each variable independently. To overcome the drawbacks, we propose an adaptive sub-sequence clustering method based on single layered Self-Organizing Incremental Neural Netwo...
