Basic Study on the Classification of Time Series Data Using a Frequency Integrated Spherical Hidden Markov Self Organizing Map
Gen Niina, Hiroshi Dozono, and Kazuhiro Muramatsu
Faculty of Science and Engineering, Saga University
1-Honjyo, Saga 840-8502, Japan
The rapid progress in and the expanding complexity of information and technology systems have made data analysis increasingly relevant. Data having a variety of elements are complex, and making very difficult to evaluate a state of a model from observed data generated probabilistically by the model. To evaluate these hidden states, we propose Spherical-Self Organizing Map (S-SOM) with a Hidden Markov Model (HMM) that infers such hidden states.
-  A. Takahashi, S. Kanaya, M. Kinouchi, T. Kozuki, and T. Ikemura, “Informatics for unrevealing hidden genome signatures,” Genome Research, Vol.13, pp. 693-702, 2003.
-  H. Dozono, T. Kabashima, et. al., “Visualization of the Packet Flows using Self Organizing Maps,” WSEAS Trans. on Information Science & Applications, Issue.1, Vol.7, pp. 132-141, 2010.
-  T. Akuts, “Mathematical Models and Algorithms in Bioinformatics,” A Series of Algorithm Science, Vol.12, Kyoritsu Shuppan, 2007.
-  G. Niina and H. Dozono, “The Spherical Hidden Markov Self Organizing Map for Learning Time Series Data,” ICANN Part 1, pp. 563-570, 2012.
-  D. Nakatsuka and M. Oyabu, “Application of Spherical SOM in Clustering,” Proc. Workshop on Self-Organizing Maps (WSOM’03), pp. 203-207, 2003.
-  N. Yamaguchi, “Self Organizing Hidden Markov Models,” Proc. of 17th Int. Conf. on Neural Information Processing, Models and Application (ICONIP 2010), LNCS6444, Springer, 2010.
-  R. Jaziri, M. Lebbah, and et. al., “SOS-HMM Self Organizing Structure of Hidden Markov Model,” Artificial Neural Networks and Machine Learning -- ICANN 2011, LNCS6792, Springer, 2011.