TCoD: A Traveling Companion Discovery Method Based on Clustering and Association Analysis

Abstract

Widely used mobile locating equipment, like phones, generates extensive spatio-temporal data every day. Since the data implicitly reflects behavior characteristics of moving objects, many applications focus on trajectory data mining while traveling companion discovery is one of the most fundamental techniques in these areas. Previous work based on time snapshot slicing have yielded some success on finding companions in regularly and frequently sampled trajectory data. However, it does not work well when data is sparse. This situation commonly appears in real world because of equipment failure or manual intervention. In this paper, we propose a novel Traveling Companion Discovery (TCoD) method that can discovery travelling companions even when time gaps between sample data are more than hours. TCoD combines density clustering and association analysis, while density clustering mine potential sets from perspective of location and association analysis identify related objects in potential sets. The evaluation on two real trajectory data sets shows that TCoD effectively overwhelms previous work with satisfying discovery of long-term companion patterns in sparse trajectory data.