Implementation of Data Mining Decision Tree Algorithms on Mobile Computing Environment
Neha Sobti1, Ketki Arora2

1Neha Sobti, Research Scholar, Lovely Professional University.
2Ketki Arora,  Assistant Professor, Lovely Professional University.

Manuscript received on 20 May 2014 | Revised Manuscript received on 25 May 2014 | Manuscript published on 30 May 2014 | PP: 28-31 | Volume-3 Issue-2, May 2014 | Retrieval Number: B1064053214/2014©BEIESP
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Abstract: The idea of complex activity for characterizing the continuously changing complex behavior patterns of mobile users. For the purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the co-occurrence of location and service, or the interleaving of all above. An activity may be composed of sub activities. We, therefore, propose new methods for complex activity mining, incremental maintenance, online detection and proactive data management based on user activities. In particular, we devise prefetching and pushing techniques with cost-sensitive control to facilitate predictive data allocation. Preliminary implementation and simulation results demonstrate that the proposed framework and techniques can significantly increase local availability, conserve execution cost, reduce response time, and improve cache utilization. Different activities may exhibit dependencies that affect user behaviors. We argue that the complex activity concept provides a more precise, rich, and detail description of user behavioral patterns which are invaluable for data management in mobile environments. Proper exploration of user activities has the potential of providing much higher quality and personalized services to individual user at the right place on the right time. With the help of data mining algorithms, we will try to reduce execution time, find correctly classified instance, reduce error rate and improve accuracy.
Keywords: ID3, DTNA, Mobile Environment, Data Mining Algorithms

Scope of the Article: Data Mining