SGPM: Static Group Pattern Mining using apriori-like sliding window

• Main Authors: GOH, John, Taniar, David, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Static method; User behavior; Mobile computing; Sliding window; Localization; Database; Data type; Knowledge engineering; Data field; Information extraction; Data analysis; Data mining; Knowledge discovery; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/895; https://ink.library.smu.edu.sg/context/sis_research/article/1894/viewcontent/Goh2006_Chapter_SGPMStaticGroupPatternMiningUs.pdf
• Institution: Singapore Management University

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.