Efficient Clustering and Prediction of Mobile Behavior Pattern in Mobile Computing System
A new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes and also to improve the over all performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of result obtained. Similarities between users are evaluated by the proposed measure, the Location-Based service alignment. Then using the similarities matrix user cluster are constructed by a novel algorithm named cluster- object based smart cluster affinity search technique (CO-SMART-CAST). Then meanwhile a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. Using the user cluster table and time interval table, cluster based temporal mobile sequential patterns (CTMPS) are generated and using the patterns the Next behaviour of user is predicted by the prediction engine efficiently.
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