Abstract
Mobile users behavior patterns is one of the most critical issues that need to be explored in mobile agent systems. Recently the algorithms of discovering frequent mobile user’s behavior patterns have been studied extensively. Existing mining methods have proposed frequent mobile user's behavior patterns statistically based on requested services and location information. Therefore, other studies considered that the mobile user's dynamic behavior patterns are usually associated with temporal access patterns. In this paper, temporal mobile access pattern methods are studied and compared in terms of complexity and accuracy. The advantages and disadvantages of these methods will be summarized as well.
Highlights
The mobile industry has experienced significant growth during the past two decades
Temporal mobile sequential pattern (TMSP)-Mine algorithm determines the large transactions for each time interval and cell
TMSPMine algorithm uses the mapping table to transform the mobile transaction sequences into maximal large transaction sequences
Summary
Abstract—Mobile users behavior patterns is one of the most critical issues that need to be explored in mobile agent systems. The algorithms of discovering frequent mobile user’s behavior patterns have been studied extensively. Existing mining methods have proposed frequent mobile user's behavior patterns statistically based on requested services and location information. Other studies considered that the mobile user's dynamic behavior patterns are usually associated with temporal access patterns. Temporal mobile access pattern methods are studied and compared in terms of complexity and accuracy. The advantages and disadvantages of these methods will be summarized as well
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