Mobility Data Mining and Knowledge Discovery

Abstract

Knowledge discovery in trajectory databases is full of success stories in discovering interesting behavioral patterns of moving objects that can be exploited in several fields. Example domains include traffic engineering, climatology, social anthropology and zoology, implying application of the various mining techniques in vehicle position data, hurricane track data, human and animal movement data, respectively. Mobility data mining can be categorized according to the underlying mining methods used to discover the various collective behavioral patterns. Following this categorization method, there have been proposed works that try to identify various types of clusters of moving objects. Some methods group trajectories by considering the whole lifespan of the moving objects, while others try to identify local patterns that are valid only for a portion of their lifespan. Another line of research, which is parallel to that of clustering, focuses on representing a dataset of trajectories via an appropriate small set of objects, which are either artificial (i.e. the representatives or centroid trajectories of the clusters), or selected from the dataset itself (i.e. by some sampling methodology). Although clustering-oriented approaches prevail in the literature, there are many other interesting techniques that exhibit semantically rich mobility patterns and make the domain active in many areas of knowledge discovery. Among them, in this chapter we discuss sequential trajectory patterns discovery, classification and outlier detection techniques. The problem of predicting the future location of the moving objects has also been tackled and presented interesting results.