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.

[1] L. Ji and M.S. Corson, "Differential Destination Multicast: A MANET Multicast Routing Protocol for Small Groups," Proc. IEEE
INFOCOM '01, Apr. 2001.
[2] R. Beraldi and R. Baldoni, "A Caching Scheme for Routing in Mobile Ad Hoc Networks and Its Application to ZRP," IEEE Trans.
Computers, vol. 52, no. 8, pp. 1051-1062, Aug. 2003.
[3] W.Su, S.Lee, and M.Gerla, "Mobility prediction in wireless networks." In MILCOM 2000. 21st Centuary Military Communications
Conference Proceedings, vol. 1, 2000.
[4] L.Bajaj, M.Takai, R.Ahuja, K.Tang, R.Bagrodia, and M.Gerla, "GloMoSim: A Scalable network simulation environment," UCLA
Computer Science Dept Technical Report, vol. 990027, 1999.
[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.
[9] Y.Choi, B.Kim, K.Jung, H.Cho, and S.Kim, " An Overlay Multicast mechanism using single-hop clustering and tree division for mobile
ad-hoc networks," in IEEE 63rd Vehicular Technology Conference, 2006. VTC 2006-Spring, vol. 2, 2006.
[10] J.J. Garcia-Luna-Aceves and E. Madruga, "The Core-Assisted Mesh Protocol," IEEE J. on Selected Areas in Comm., vol. 17, no. 8,
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
[2] David Hand, Heikki Mannila and Padhraic Smyth, Principles of data mining (Cambridge, MA: MIT Press, 2001)
[3] Jiawei Han and Micheline Kamber, Data mining-concepts and techniques (San Francisco CA, USA, Morgan Kaufmann Publishers,
2001)
[4] A.K. Jain, R.C. Dubes, Algorithms for clustering data, (Englewood Cliffs, NJ: Prentice-Hall, 1998)
[5] M.R. Anderberg, Cluster analysis for application, (Academic Press, New York, 1973)
[6] J.A. Hartigan, Clustering Algorithms, (Wiley, New York, 1975)
[7] Hand, D.J., Blunt, G., Kelly, M.G. & Adams, N.M. (2000), Data mining for fun and profit, (Statistical Science) 15, 111-131.
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of flexible manipulator arms: A tutorial review, Proc. 29th IEEE Conf. on Decision and Control, San Francisco, CA, 1990, 500-506
[9] Aggarwal, Charu C., Han,Jiawei,Wang, Jianyong, & Yu, Philip S. A framework for clustering evolving data streams, VLDB
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

Abstract:Our project is an Android application based on Virtual Network Computing" which helps people to access their laptops, personal computers using smartphones over a wireless network. Features like wireless data transfer between the android device and the client are also supported. The Integrated development environment used was Eclipse with the android plugin, and the server side (your laptop,pc etc.) programming was done using java. Client -server architecture was established for two way communication purpose where client was the android Smartphone and server was the windows desktop which communicate over TCP layer. The android client takes inputs from the users and sends the protocols for the execution of the mouse, keyboard and zooming events. The server parses such protocols and executes them using robot class of java. The desktop in turn takes screenshots continuously using robot class and sends them to the smartphone, thus establishing a two way communication network. The server sends the user interface to the client using the remote frame buffer. Remote frame buffer is the protocol used in virtual network computing. At the same time server receives commands from the client in the form of protocols. Since both the processes need to be performed simultaneously two threads are created, one to receive protocols from the client and the other to send user interface to the client.
Keywords:Protocol, Virtual NetworkComputing, Screenshots, Client-Server, Zooming-Panning, Mouse Driver

[1] http://cursos.leon.uia.mx/temporal/software/vnc/windows/tr.98.1.pdf
[2] www.realvnc.com/docs/rfbproto.pdf
[3] www.realvnc.com/products/vnc/.../5.../VNCUser_Guide.pdf
[4] www.wikipedia.org/wiki/Virtual_Network_Computing
[5] www.wikipedia.org/wiki/RFB_protocol

Abstract:In this paper, an efficient real time algorithm for segmenting celestial objects from astronomical images is proposed. The proper segmentation of astronomical objects like planets, comets, galaxies, asteroids etc. is a difficult task due to the presence of innumerous bright point sources in the frame, presence of noise, weak edges of celestial objects, low contrast etc. In order to overcome these bottlenecks, multiple preprocessing steps are performed on the actual image prior to segmenting the desired object(s). Level Set segmentation is the key technique of this proposed method. The result of the proposed algorithm on various celestial objects substantiates the effectiveness of the proposed method.
Keywords:Classification, Level-set segmentation, Pattern recognition, TV denoising, Wavelet Transform.

[1] Emmanuel Bertin, Mining pixels: The extraction and classification of astronomical sources, Mining the Sky Eso astrophysics
symposia 2001, pp 353-371
[2] P. Suetnes, P.Fua and A. J. Hanson, Computational strategies for object recognition, ACM Computing Surveys, Vol. 24, pp. 05-
61, 1992.
[3] Venkatadri.M, Dr. Lokanatha C. Reddy, A Review on Data mining from Past to the Future, International Journal of Computer
Applications, Volume 15– No.7, February 2011, 112-116.
[4] Jean-Luc Starck and Fionn Murtagh, Handbook of Astronomical image and Data Analysis, Springer-Verlag-2002
[5] E. Aptoula, S. Lef`evre and C. Collet, Mathematical morphology applied to the segmentation and classification of galaxies in
multispectral images, European Signal Processing Conference (EUSIPCO), Italy (2006)"
[6] Dibyendu Ghoshal, Pinaki Pratim Acharjya, A Modified Watershed Algorithm for Stellar Image, International Journal of Computer
Applications, Volume 15– No.7, February 2011, 112-116.
[7] M. Frucci and G. Longo, Watershed transform and the segmentation of astronomical images. In Proceedings of Astronomical Data Analysis III, Naples, Italy, April 2004. Qriginal Image Segmented Output Original image Segmented Output
[8] Gonzales, Digital image processing (Pearson Education India, 2009)
[9] K.P Soman, K.I Ramachandran, N.G Reshmi, Insight into wavelet transform, PHI Learning Pvt. Ltd., 2010
[10] A. Manjunath, H.M.Ravikumar,Comparison of Discrete Wavelet Transform (DWT), Lifting Wavelet Transform (LWT) Stationary
Wavelet Transform (SWT) and S-Transform in Power Quality Analysis, European Journal of Scientific Research, Vol.39 No.4
(2010), pp.569-576

Abstract:A Spatial data preference query ranks the given objects based on the quality and their features in nearest neighborhood. Here "feature" refers to a class of objects in a spatial map such as specific facilities or services. For example, a real estate agency maintains database which holds the details of flats for rent. Here, A customer may want to rank the contents with respect to the appropriateness of their locations, the ranks are given by using the top-k spatial aggregate functions with respect to quality of features and nearest neighborhood. A neighborhood concept can be specified by the user via different functions. Every customer maximum prefers the quality of the flat and more facilities in that flat. The potential customer wishes to view the top 10 flats with the largest size and lowest prices, which is nearest to Hospital, School, Bus top, Market etc. In this paper, we define top-k spatial preference queries and branch and bound algorithm.
Keywords: Spatial data, Top-k spatial.

[1] M.L. Yiu, X. Dai, N. Mamoulis, and M. Vaitis, "Top-k Spatial Preference Queries," Proc. IEEE Int'l Conf. Data Eng. (ICDE),
2007.
[2] N. Bruno, L. Gravano, and A. Marian, "Evaluating Top-k Queries over Web-Accessible Databases," Proc. IEEE Int'l Conf. Data
Eng. (ICDE), 2002.
[3] A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Proc. ACM SIGMOD, 1984.
[4] G.R. Hjaltason and H. Samet, "Distance Browsing in Spatial Databases," ACM Trans. Database Systems, vol. 24, no. 2, pp. 265-
318, 1999.
[5] R. Weber, H.-J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-
Dimensional Spaces," Proc. Int'l Conf. Very Large Data Bases (VLDB), 1998.
[6] K.S. Beyer, J. Goldstein, R. Ramakrishna, and U. Shaft, "When is' Nearest Neighbor'
[7] Meaningful?" Proc. Seventh Int'l Conf. Database Theory (ICDT), 1999.
[8] R. Fagin, A. Lotem, and M. Naor, "Optimal Aggregation Algorithms for Middleware," Proc. Int'l Symp. Principles of Database
Systems (PODS), 2001.
[9] I.F. Ilyas, W.G. Aref, and A. Elmagarmid, "Supporting Top-k Join Queries in Relational Databases," Proc. 29th Int'l Conf. Very
Large Data Bases (VLDB), 2003.
[10] N. Mamoulis, M.L. Yiu, K.H. Cheng, and D.W. Cheung, "Efficient Top-k Aggregation of Ranked Inputs," ACM Trans. Database
Systems, vol. 32, no. 3, p. 19, 2007.

Abstract:Data mining is widely used domain for extracting trends or patterns from historical data. However, the databases used by enterprises can't be directly used for data mining. It does mean that Data sets are to be prepared from real world database to make them suitable for particular data mining operations. However, preparing datasets for analyzing data is tedious task as it involves many aggregating columns, complex joins, and SQL queries with sub queries. More over the existing aggregations performed through SQL functions such as MIN, MAX, COUNT, SUM, AVG return a single value output which is not suitable for making datasets meant for data mining. In fact these aggregate functions are generating vertical aggregations. This paper presents techniques to support horizontal aggregations through SQL queries. The result of the queries is the data which is suitable for data mining operations. It does mean that this paper achieves horizontal aggregations through some constructs built that includes SQL queries as well. The methods prepared by this paper include CASE, SPJ and PIVOT. We have developed a prototype application and the empirical results reveal that these constructs are capable of generating data sets that can be used for further data mining operations.
Keywords:Aggregations, SQL, data mining, OLAP, and data set generation.s

[1] C. Ordonez, "Data Set Preprocessing and Transformation in a Database System," Intelligent Data Analysis, vol. 15, no. 4, pp. 613-
631, 2011.
[2] C. Ordonez, "Statistical Model Computation with UDFs," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 12, pp. 1752 -1765,
Dec. 2010.
[3] C. Ordonez and S. Pitchaimalai, "Bayesian Classifiers Programmed in SQL," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 1 ,
pp. 139-144, Jan. 2010.
[4] J. Han and M. Kamber, Data Mining: Concepts and Techniques, first ed. Morgan Kaufmann, 2001.
[5] C. Ordonez, "Integrating K-Means Clustering with a Relational DBMS Using SQL," IEEE Trans. Knowledge and Data Eng., vol.
18, no. 2, pp. 188-201, Feb. 2006.
[6] S. Sarawagi, S. Thomas, and R. Agrawal, "Integrating Association Rule Mining with Relational Database Systems: Alternatives and
Implications," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '98), pp. 343-354, 1998.
[7] H. Wang, C. Zaniolo, and C.R. Luo, "ATLAS: A Small But Complete SQL Extension for Data Mining and Data Streams," Proc.
29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 1113- 1116, 2003.
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RDBMS for OLAP," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '03), pp. 52 -63, 2003.
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Abstract:New security threats emerge against mobile devices as the devices' computing power and storage capabilities evolve. Preventive mechanisms like authentication, encryption alone are not sufficient to provide adequate security for a system. There is a definite need for Intrusion detection systems that will improve security and use fewer resources on the mobile phone. In this work we proposed an intrusion detection method that efficiently detects intrusions in mobile phones using Data Mining techniques. We used network based approach that will remove the overhead processing from the mobile phones. A neural network classifier will be built and trained for each user based on his call logs .An application that runs on smart phone of the user collects certain information of the user and sends them over to the remote server. These logs then fed to the already trained classifier which analyzes the logs and sends back the feedback to the smart phones whenever abnormalities are found. Also we compared different neural classifiers to identify the classifier with better performance. Our results showed clearly the effectiveness of our method to detect intrusions and outperformed existing Intrusion detection methods with 95% detection rate.

[1] Azzedine Boukerche, Mirela Sechi M, Annoni Notare "Behavior-Based Intrusion Detection in Mobile Phone Systems", Journal of
Parallel and Distributed Computing 62, pp.1476–1490, 2002.
[2] Shabtai, A., et al. Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method. J. Syst.Software
(2010), doi:10.1016/j.jss.2010.03.046
[3] Didier Samfat, Refik Molva, "IDAMN: an Intrusion Detection Architecture for Mobile Networks", INSTITUT EURÉCOM, France
[4] Markus Miettinen, Perttu Halonen, Kimmo Hatonen, "Host-Based Intrusion Detection for Advanced Mobile Devices", Nokia
Research Center, IEEE, 2006.
[5] S. Shimojo et al. (Eds.): HSI 2005, LNCS 3597, pp. 57 – 65, 2005. Springer-Verlag Berlin Heidelberg 2005
[6] Wenkee Lee, "Intrusion Detection Techniques for Mobile Wireless Networks", Mobile Networks and Applications, 2003.
[7] Eugene spafford, Diego Zamboni, "Data collection mechanisms for intrusion detection systems", CERIAS Technical Report, West
Lafayette, IN, 2000.
[8] Jerry Cheng, Starsky H.Y.Wong, Hao Yang and Songwu Lu, "SmartSiren: Virus Detection and Alert for Smartphones",
MobiSys'07, San Juan, Puerto Rico, 2007.
[9] Barak Pearlmutter, Christina Warrender, Stephanie Forrest, "Detecting Intrusions Using System Calls:Alternative Data Models",
New Mexico., 2010.