Ethological data mining: an automata-based approach to extract behavioral units and rules
| Autor: | Miki Takahasi, Tetsuro Nishino, Yasuki Kakishita, Kazutoshi Sasahara, Kazuo Okanoya |
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| Rok vydání: | 2008 |
| Předmět: |
Structure (mathematical logic)
Computer program Computer Networks and Communications Computer science business.industry Behavioral pattern Machine learning computer.software_genre Computer Science Applications Automaton Set (abstract data type) Behavioral data Core (graph theory) Sequential data Data mining Artificial intelligence business computer Information Systems |
| Zdroj: | Data Mining and Knowledge Discovery. 18:446-471 |
| ISSN: | 1573-756X 1384-5810 |
| DOI: | 10.1007/s10618-008-0122-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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