Title

SGPM: Static Group Pattern Mining using Apriori-like Sliding Window

Publication Type

Conference Proceeding Article

Publication Date

4-2006

Abstract

Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead to find group patterns thus reducing the complexity of the mining problem.

Keywords

Static method, User behavior, Mobile computing, Sliding window, Localization, Database, Data type, Knowledge engineering, Data field, Information extraction, Data analysis, Data mining, Knowledge discovery

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12: Proceedings

Volume

3918

First Page

415

Last Page

424

ISBN

9783540332077

Identifier

10.1007/11731139_48

Publisher

Springer Verlag

City or Country

Singapore

Additional URL

http://dx.doi.org/10.1007/11731139_48

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