Accurately clustering moving objects with adaptive history filtering

This paper addresses the problem of detecting and tracking clusters of moving objects in spatio-temporal data sets. Spatio-temporal data sets contain data objects that move in space over time. Traditional data clustering algorithms work well on static data sets that contain well separated clusters. Traditional techniques breakdown when they are applied to spatio-temporal data sets. They are not capable of tracking clusters when the moving objects intersect the space occupied by objects from another cluster. This work aims to improve the accuracy of traditional data clustering algorithms on spatio-temporal data sets. Many clustering algorithms create clusters based on the distance between the objects. We extend this distance measure to be a function of the position history of the objects. We show through a series of experiments that the use of the history based distance measures greatly improves the accuracy of existing data clustering algorithms on spatio-temporal data sets. In random data sets we achieve up to a 90% improvement in cluster accuracy. To evaluate the clustering algorithms we created 100 spatio-temporal data sets. We also defined a set of metrics that are used to evaluate the performance of the clustering algorithms on the spatio-temporal data sets.