Data Categorization and Noise Analysis in Mobile Communication Using Machine Learning Algorithms


Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base stations and also the noise levels in the busy hour can be predicted. J48 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari. Signal to noise ratio has been predicted at 55, based on CART results. About 53% instances provided inside the cluster and 47% provided outside the cluster. DBScan clustering provided maximum noise from srikakulam. MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.

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R. Phani Kumar, M. Rao and D. Kaladhar, "Data Categorization and Noise Analysis in Mobile Communication Using Machine Learning Algorithms," Wireless Sensor Network, Vol. 4 No. 4, 2012, pp. 113-116. doi: 10.4236/wsn.2012.44015.

Conflicts of Interest

The authors declare no conflicts of interest.


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