Mining Parallel Patterns from Mobile Users

Mining Parallel Patterns from Mobile Users

John Goh (Monash University, Australia) and David Taniar (Monash University, Australia)
DOI: 10.4018/jbdcn.2005010104
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Abstract

Mobile technology is a widely adopted technology and is used for mobile users to stay connected. Mobile data mining is an extension of data mining for the purpose of developing innovative ways to extract useful knowledge out from mobile users to the decision makers. Our proposed method, parallel pattern, provides a method for finding out similar decisions by mobile users. Parallel patterns are divided into physical parallel pattern and logical parallel pattern. Physical parallel pattern finds the similarities of physical movement decisions among mobile users. Logical parallel pattern finds the logical similarities of logical theme movements among mobile users. In physical parallel pattern, mobile users can only occupy one physical location at any one time while in logical parallel pattern, mobile users can occupy more than one logical theme at any one time and each static node can have more than one logical theme at any one time. Our performance evaluation shows that the method for mobile parallel pattern mining suits for the real world problem in both the physical and logical parallel pattern mobile mining paradigm.

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