Advanced data mining method for discovering regions and trajectories of moving objects: "Ciconia Ciconia" scenario

Abstract

Trajectory data is of crucial importance for a vast range of applications involving analysis of moving objects behavior. Unfortunately, the extraction of relevant knowledge from trajectory data is hindered by the lack of semantics and the presence of errors and uncertainty in the data. This paper proposes a new analytical method to reveal the behavioral characteristics of moving objects through the representative features of migration trajectory patterns. The method relies on a combination of Fuzzy c-means, Subtractive and Gaussian Mixture Model clustering techniques. Besides, this method enables splitting the analysis into sections in order to differentiate the whole migration into i) migration-to-destination, ii) reverse-migration. The method also identifies places where moving objects' cumulate and increase in number during the moves (bottleneck points). It also computes the degree of importance for a given point or probability of existence of an object at a given coordinate within a certain confidence degree, which in turn determines certain zones having different degrees of importance for the move, i.e. critical zones of interest. As shown in this paper, other techniques are not capable to elaborate similar results. Finally, we present experimental results using a trajectory dataset of migrations of white storks (Ciconia ciconia).