bookmarks  338

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    . Chemical Earthing Electrodes 40 (1): 1 (January 2018)
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      . (2018`)Contact Me to know more about Pega Training: Call: +91-9908107432 Mail : ashock.pegatraining@gmail.com Website: https://pegazone.blogspot.in/.
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        , and . IRJCS:: International Research Journal of Computer Science Volume IV (Issue XII): 01-06 (December 2017)1. S. Berchtold, C Bohm, and H. Kriegel. The Pyramid-Technique: Towards Breaking the Curse of Dimensionality. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pages 142–153, Seattle, Washington, 2010, 98. 185 2. Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. The SR-tree : An index structure for high-dimensional data. In Proceedings of 22th International Conference on Very Large Data Bases, VLDB’12, pages 28–39, Bombay, India, 2012. 3. N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger. The SR-tree: an Efficient and Robust Access Method for Points and Rectangles. In Proceedings of ACM-SIGMOD International Conference on Management of Data, pages 322–331, Atlantic City, NJ, May 2011. 4. K. Chakrabarti and S. Mehrotra. The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces. In Proceedings of the 16th International Conference on Data Engineering, pages 440–447, San Diego, CA, February 2012. 5. Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. Cure: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD conference on Management of Data, pages 73–84, Seattle, WA, 2011. 6. R. Kurniawati, J. S. Jin, and J. A. Shepherd. The SS+-tree: An improved index structure for similarity searches in a high-dimensional feature space. In Proceedings of SPIE Storage and Retrieval for Image and Video Databases, pages 13–24, February 2012. 7. N. Katayama and S. Satoh. The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pages 369–380, Tucson, Arizona, 2013. 8. J.T. Robinson. The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 10–18, Ann Arbor, MI, April 2013. 9. D.A. White and R. Jain. Similarity Indexing with the SS-tree. In Proceedings of the 12th Intl. Conf. on Data Engineering, pages 516–523, New Orleans, Louisiana, February 2014. 10. D. Yu, S. Chatterjee, G. Sheikholeslami, and A. Zhang. Efficiently detecting arbitrary shaped clusters in very large datasets with high dimensions. Technical Report 98-8, State University of New York at Buffalo, Department of Computer Science and Engineering, November 2013. 11. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pages 103–114, Montreal, Canada, 2012..
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          , , , , and . IJIRIS:: International Journal of Innovative Research Journal in Information Security Volume IV (Issue XII): 01-07 (December 2017)1 Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. “Bayesian CART Model Search.” Journal of the American Statistical Association, Vol. 93(443), pp 935–948, September 1998. 2 Sujata Garera, Niels Provos, Monica Chew, and Aviel D. Rubin. “A framework for detection and measurement of phishing attacks.” In Proceedings of the 2007 ACM workshop on Recurring malicious code - WORM ’07, page 1, 2007. 3 Abhishek Gattani, AnHai Doan, Digvijay S. Lamba, NikeshGarera, Mitul Tiwari, Xiaoyong Chai, Sanjib Das, Sri Subramaniam, AnandRajaraman, and VenkyHarinarayan. “Entity extraction, linking, classifica- tion, and tagging for social media.” Proceedings of the VLDB Endowment, Vol. 6(11), pp 1126–1137, August 2013. 4 David D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. pages 4–15. 1998. 5 Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. “Learning to detect malicious URLs.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), pp 1–24, April 2011. 6 FadiThabtah Maher Aburrous, M.A.Hossain, KeshavDahal. “Intelligent phishing detection system for e-banking using fuzzy data mining.” Expert Systems with Applications, Vol. 37(12), pp 7913–7921, Dec 2010. 7 AnkushMeshram and Christian Haas. “Anomaly Detection in Industrial. Networks using Machine Learning: A Roadmap.” In Machine Learning for Cyber Physical Systems, pages 65–72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2017. 8 Xuequn Wang Nik Thompson,Tanya Jane McGill. “Security begins at home: Determinants of home computer and mobile device security behavior.” Computers & Security, Vol. 70, pp 376–391, Sep 2017. 9 Dan Steinberg and Phillip Colla. “CART: Classification and Regression Trees.” The Top Ten Algorithms in Data Mining, pp 179–201, 2009. 10 D. Teal. “Information security techniques including detection, interdiction and/or mitigation of memory injection attacks,” Google patents. Oct 2013. 11 Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. “Design and Evaluation of a Real-Time URL Spam Filtering Service.” In 2011 IEEE Symposium on Security and Privacy, pp 447–462. May 2011. 12 Sean Whalen, Nathaniel Boggs, and Salvatore J. Stolfo. “Model Aggregation for Distributed Content Anomaly Detection.” In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec ’14, pp 61–71, New York, USA, 2014. ACM Press. 13 Ying Yang and Geoffrey I. Webb. “Discretization for Naive-Bayes learning: managing a discretization bias and variance.” Machine Learning, Vol. 74(1), pp 39–74, Jan 2009..
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