Ethological data mining: an automata-based approach to extract behavioral units and rules

Authors:
Yasuki Kakishita;Kazutoshi Sasahara;Tetsuro Nishino;Miki Takahasi;Kazuo Okanoya
Affiliations:
Department of Information and Communication Engineering, Graduate School of Electro-Communications, The University of Electro-Communications, Tokyo, Japan 182-8585 and Embedded System Platform Res ...;Laboratory for Biolinguistics, RIKEN Brain Science Institute, Saitama, Japan 351-0198 and Japan Society for the Promotion of Science, Tokyo, Japan;Department of Information and Communication Engineering, Graduate School of Electro-Communications, The University of Electro-Communications, Tokyo, Japan 182-8585;Laboratory for Biolinguistics, RIKEN Brain Science Institute, Saitama, Japan 351-0198;Laboratory for Biolinguistics, RIKEN Brain Science Institute, Saitama, Japan 351-0198
Venue:
Data Mining and Knowledge Discovery
Year:
2009

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Abstract

We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. By introducing novel extensions, we integrate two elemental methods--the N-gram model and Angluin's machine learning algorithm into an ethological data mining framework. This allows us to obtain the minimized automaton-representation of behavioral rules that accept (or generate) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data; we use the Bengalese finch song and perform experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining works effectively even for noisy behavioral data by appropriately setting the parameters that we introduce. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branches.