Mining periodic behaviors for moving objects

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a twostage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the sec...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays, Peter Nye
• Keywords: Data Mining, KDD 2010, Mining Periodic Behaviors, Periodic Behavior, Reference Spot