Fast Agglomerative Information Bottleneck Based Trajectory Clustering

Abstract

Clustering is an important data mining technique for trajectory analysis. The agglomerative Information Bottleneck (aIB) principle is efficient for obtaining an optimal number of clusters without the direct use of a trajectory distance measure. In this paper, we propose a novel approach to trajectory clustering, fast agglomerative Information Bottleneck (faIB), to speed up aIB by two strategies. The first strategy is to do “clipping” based on the so-called feature space, calculating information losses only on fewer cluster pairs. The second is to select and merge more candidate clusters, reducing iterations of clustering. Remarkably, faIB considerably runs above 10 times faster than aIB achieving almost the same clustering performance. In addition, extensive experiments on both synthetic and real datasets demonstrate that faIB performs better than the clustering approaches widely used in practice.

This work has been funded by Natural Science Foundation of China (61471261, 61179067, U1333110), and by grants TIN2013-47276-C6-1-R from Spanish Government and 2014-SGR-1232 from Catalan Government (Spain).