ABSTRACT 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.
Cite this paper
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.
 Fabrizio Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys (CSUR), Vol. 34, No. 1, 2002, pp. 1-47. doi:10.1145/505282.505283
 Leland Wilkinson, “The Future of Statistical Computing,” Technometrics, Vol. 50, No. 4, 2008, pp. 418-435.
 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey,” Computer Networks, Vol. 38, No. 4, 2002, pp. 393-422.
 D. S. V. G. K. Kaladhar, T. Uma Devi, P. V. Lakshmi, R. Harikrishna Reddy, R. K. SriTeja Ayayangar V. and P. V. Nageswara Rao, “Analysis of E. coli Promoter Regions Using Classification, Association and Clustering Algorithms,” Advances in Intelligent and Soft Computing, Vol. 132, 2012, pp. 169-177.
 J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1, 1986, pp. 81-106.
 M. K. Anderberg, “Cluster Analysis for Applications,” Academic Press, Waltham, 1973.
 R. S. Michalski and R. E. Strepp, “Automated Construction of Classification: Conceptual Clustering versus Numerical Taxonomy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 5, No. 4, 1983, pp. 396-410. doi:10.1109/TPAMI.1983.4767409
 D. S. V. G. K. Kaladhar and B. Chandana, “Data Mining, Inference and Prediction of Cancer Datasets Using Learning Algorithms,” International Journal of Science and Advanced Technology, Vol. 1, No. 3, 2011, pp. 68-77.
 T. J. Wang, B. S. Yang, J. Gao, D. Q. Yang, S. W. Tang, H. Y. Wu, K. D. Liu and J. Pei, “MobileMiner: A Real World Case Study of Data Mining in Mobile Communication,” Proceedings of the 35th SIGMOD International Conference on Management of Data, Rhode Island, 29 June-2 July 2009. pp. 1083-1086.
 T. Menzies and Ying Hu, “Data Mining for Very Busy People,” Computer, Vol. 36, No. 11, 2003, pp. 22-29.
 Wen-Chih Peng and Ming-Syan Chen, “Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System,” IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 1, 2003, pp. 70-85. doi:10.1109/TKDE.2003.1161583
 N. Eagle, A. Pentland and D. Lazer, “Inferring Friendship Network Structure by Using Mobile Phone Data,” Proceedings of the National Academy of Sciences, Vol. 106, No. 36, 2009, pp. 15274-15278.
 J. Goh and D. Taniar, “Mining Frequency Pattern from Mobile Users,” Knowledge-Based Intelligent Information and Engineering Systems, Vol. 3215, 2004, pp. 795-801.
 Y. Chi, R. R. Muntz, S. Nijssen and J. N. Kok, “Frequent Subtree Mining—An Overview,” Fundamental Informaticae—Advances in Mining Graphs, Trees and Sequences, Vol. 66, No. 1-2, 2004, pp. 161-198.
 J. Y. Goh and D. Taniar, “Mobile Data Mining by Location Dependencies,” Intelligent Data Engineering and Automated Learning, Vol. 3177, 2004, pp. 225-231