Mining top-k regular episodes from sensor streams

Komate Amphawan (1) , Julie Soulas (2, 3) , Philippe Lenca (2, 3)

1 CIL - Computational Innovations Laboratory (Faculty of Informatics, Burapha University)
2 Lab-STICC_TB_CID_DECIDE
3 LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information

The monitoring of human activities plays an important role in health-care applications and for the data mining community. Existing approaches work on activities recognition occurring in sensor data streams. However, regular behaviors have not been studied. Thus, we here introduce a new approach to discover top-k most regular episodes from sensors streams, TKRES. The top-k approach allows us to control the size of the output, thus preventing overwhelming result analysis for the supervisor. TKRES is based on the use of a simple top-k list and a k-tree structure for maintaining the top-k episodes and their occurrence information. We also investigate and report the performances of TKRES on two real-life smart home datasets.

Mots clés: Activities of daily living; Episode discovery; Data stream; Regular patterns

Domaines: Algorithme et structure de données [cs.DS]; Intelligence artificielle [cs.AI]; Ordinateur et société [cs.CY]; Machine Learning [stat.ML]

Komate Amphawan, Julie Soulas, Philippe Lenca. Mining top-k regular episodes from sensor streams. IAIT 2015 : 7th International Conference on Advances in Information Technology, Nov 2015, Bangkok, Thailand. pp.76 - 85, ⟨10.1016/j.procs.2015.10.008⟩. ⟨hal-01247461⟩