Title

Efficient Mining of Group Patterns from User Movement Data

Publication Type

Journal Article

Publication Date

2006

Abstract

In this paper, we present a new approach to derive groupings of mobile users based on their movement data. We assume that the user movement data are collected by logging location data emitted from mobile devices tracking users. We formally define group pattern as a group of users that are within a distance threshold from one another for at least a minimum duration. To mine group patterns, we first propose two algorithms, namely AGP and VG-growth. In our first set of experiments, it is shown when both the number of users and logging duration are large, AGP and VG-growth are inefficient for the mining group patterns of size two. We therefore propose a framework that summarizes user movement data before group pattern mining. In the second series of experiments, we show that the methods using location summarization reduce the mining overheads for group patterns of size two significantly. We conclude that the cuboid based summarization methods give better performance when the summarized database size is small compared to the original movement database. In addition, we also evaluate the impact of parameters on the mining overhead.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Data and Knowledge Engineering

Volume

57

First Page

240

Last Page

282

ISSN

0169-023X

Identifier

10.1016/j.datak.2005.04.006

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/j.datak.2005.04.006