iosr-JCE   Volume-6 ~ Issue-5

Abstract:In this paper, the author has studied the performance of multicast routing protocols in wireless mobile ad-hoc networks. In MANET, for a protocol to be more efficient and high robust is a difficult task, due to the mobility of nodes and dynamic topology. It discovers the routing path by broadcasting message over the whole network, which results in considerable cost for routing discovery and maintenance. Moreover, the reliability of the discovered path can not be guaranteed, since the stabilities of nodes along such path are unpredictable. RBMR employs a mobility prediction based election process to construct a reliable backbone structure performing packet transmission, message broadcasting, routing discovery and maintenance. Another protocol novel Robust and Scalable Geographic Multicast Protocol (RSGM) is also analyzed. Several virtual architectures are used in the protocol without need of maintaining state information for more robust and scalable membership management and packet forwarding in the presence of high network dynamics due to unstable wireless channels and node movements. .
Keywords: Multicast routing, geographic multicast, wireless networks, mobile ad hoc networks, geographic routing, scalable, robust, mobility prediction.

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[5] D. R. Johnson and D. A. Maltz, "Dynamic source routing in adhoc wireless networks," in Mobile Computing, (ed. T. Imielinski and H.
Korth), Kluwer Academic Publishers, 19%.
[6] S.Lee, W.Su, and M.Gerla, "On-demand multicast routing protocol in multi-hop wireless mobile networks," Mobile Networks and
Applications, vol. 7, no. 6, pp. 441-453, 2002.
[7] P.Sinha, R.Sivakumar, and V.Bhargavan, "MCEDAR: Multicast Core-Extraction Distributed Ad-hoc routing." In 1999 IEEE Wireless
Communications and Networking Conferences, 1999, WCNC, 19999, pp 1313-1317
[8] J.Jetcheva and D.Johnson, "Adaptive demand driven multicast routing in multi-hop wireless ad-hoc networks," in proceedings of the
2nd ACM international symposium on mobile ad-hoc networking and computing. ACM New york, NY, USA, 2001, pp. 33-44.
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ad-hoc networks," in IEEE 63rd Vehicular Technology Conference, 2006. VTC 2006-Spring, vol. 2, 2006.
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pp. 1380-1394, Aug. 1999.

Abstract:Clustering using the basic version of the K-Means algorithm begins by randomly selecting K cluster centers, assigning each point to the cluster whose mean is closest in a Euclidean distance sense, computing the mean vectors of the points assigned to each cluster and using these as new centers in an iterative approach. This suggests that if we identify points in the dataset which represent the final unchanging means, the task of clustering reduces to just assigning the remaining points in the dataset into clusters which are closest to these final means based on the Euclidean Distance measure. Taking a cue from the result of the K-Means algorithm this paper presents an approach for performing collinear clustering based on the idea that values in a dataset can be put into different clusters, depending on which points in the dataset lie at maximum distance from each other. The clusters are formed by finding the minimum Euclidean distance of all points in the dataset and these maximally separated data points.
Keywords - Collinear clustering, Maximal distance clustering, Minimum Euclidean distance, Jmin, Jmax.

[1] Pang-Ning Tan, Michael Steinbachand Vipin Kumar, Introduction to data mining (Addison Wesley, 2006
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[3] Jiawei Han and Micheline Kamber, Data mining-concepts and techniques (San Francisco CA, USA, Morgan Kaufmann Publishers,
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Endowment, Proceedings of the 29th international conference on very large data bases, Vldb '2003, 81–92.

Abstract:Missing Data is a widespread problem that can affect the ability to use data to construct effective predictions systems. We analyze the predictive performance by comparing K-Means Clustering with kNN Classifier for imputing missing value. For investigation, we simulate with 5 missing data percentages; we found that k-NN performs better than K-Means Clustering, in terms of accuracy.
Keywords:K-Means clustering, k-NN Classifier, Missing Data, Percentage, Predictive Performance

Journal Papers:
[1] J.L Peugh, and C.K. Enders, "Missing data in Educational Research: A review of reporting practices and suggestions for
improvement, "Review of Educational Research vol 74, pp 525-556, 2004.
[2] S-R. R. Ester-Lydia , Pino – Mejias Manuel, Lopez Coello Maria-Dolores , Cubiles – de – la- Vega, "Missing value imputation on
Missing completely at Random data using multilayer perceptrons, "Neural Networks, no 1, 2011.
[3] B.Mehala, P.Ranjit Jeba Thangaiah and K.Vivekanandan , " Selecting Scalable Algorithms to Deal with Missing Values" ,
International Journal of Recent Trends in engineering, vol.1. No 2, May 2009.
[4] Gustavo E.A.P.A. Batista and Maria Carolina Monard , "A Study of K-Nearest Neighbour as an Imputation method".
[5] Allison, P.D-"Missing Data", Thousand Oaks, CA: Sage -2001.
[6] Bennett, D.A. "How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health", 25, pp.464 –
469, 2001.
[7] Kin Wagstaff ,"Clustering with Missing Values : No Imputation Required" -NSF grant IIS-0325329,pp.1-10.
[8] S.Hichao Zhang , Jilian Zhang, Xiaofeng Zhu, Yongsong Qin,chengqi Zhang , "Missing Value Imputation Based on Data
Clustering", Springer-Verlag Berlin, Heidelberg ,2008.
[9] Richard J.Hathuway , James C.Bezex, Jacalyn M.Huband , "Scalable Visual Assessment of Cluster Tendency for Large Data Sets",
Pattern Recognition ,Volume 39, Issue 7,pp,1315-1324- Feb 2006.
[10] Qinbao Song, Martin Shepperd ,"A New Imputation Method for Small Software Project Data set", The Journal of Systems and
Software 80 ,pp,51–62, 2007