Ranking Optimization using Multi-attributes Line up Algorithm

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-21 Number-1
Year of Publication : 2015
Authors : Rakesh Kumar Roshan, Piyush Singh
  10.14445/22312803/IJCTT-V21P102

MLA

Rakesh Kumar Roshan, Piyush Singh "Ranking Optimization using Multi-attributes Line up Algorithm". International Journal of Computer Trends and Technology (IJCTT) V21(1):7-13, March 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Ranking is a technique to categorize & finding the best option in the market. When number of suitable option is available in the market so its difficult to getting the best option is always a problem. In this paper we proposed a technique to optimize the ranking and its availability to check performance factor in order to maintained high ranking and quality of popular option in the market. We enhanced the line-up algorithm for ranking optimization approached, so, we used to line-up technique is demonstration to check other factor which affect to ranking of products, we are finding relevant research to get factor detail which to improve the ranking of product.

References
[1] LineUp: Visual Analysis of Multi-Attribute Rankings Samuel Gratzl, Alexander Lex, Nils Gehlenborg, HanspeterPfister, and Marc Streit.
[2] A. P. Sawant and C. G. Healey. Visualizing multidimensional query results using animation. In Electronic Imaging 2008, page 680904, 2008.
[3] L. Byron and M. Wattenberg. Stacked graphs - geometry &aesthetics.IEEE Transactions on Visualization and Computer Graphics, 14(6):1245–1252, 2008.
[4] C. Shi, W. Cui, S. Liu, P. Xu, W. Chen, and H. Qu. RankExplorer: visualization of ranking changes in large time series data. IEEE Transactions on Visualization and Computer Graphics, 18(12):2669 –2678, 2012.
[5] P. Kidwell, G. Lebanon, andW. S. Cleveland. Visualizing incomplete and partially ranked data. IEEE Transactions on Visualization and Computer Graphics, 14(6):1356–1363, 2008.
[6] E. Tufte. The Visual Display of Quantitative Information. Graphics Press, 2nd edition, 1983.
[7] M. Ward, G. Grinstein, and D. A. Keim. Interactive Data Visualization: Foundations, Techniques, and Application. A.K. Peters, 2010.
[8] Kleinberg, Jon; “Authoritative Sources in a Hyperlinked Environment;” Proc. ACM-SIAM Symposium on DiscreteAlgorithms, 1998; pp. 668-677.
[9] Madria, Sanjay Kumar; ‘Web Mining: A Bird’s Eye View;” http://mandolin.cais.ntu.edu.sg/wise2002/slides.shtml;WISE 2002, Singapore.
[10] Brin, Sergey; Page, Lawrence; “The Anatomy of a Large-Scale Hypertextual Web Search Engine;” 7th Int. WWWConf. Proceedings, Brisbane, Australia; April 1998.
[11] Chakrabarti, S. et. al.,; “Mining the link structure of the World Wide Web;” IEEE Computer, 32(8), August 1999.
[12] Baeza-Yates,Ricardo; Davis, Emilio; “Web page ranking using link attributes,” Proceedings of the 13th internationalWorld Wide Web conference on Alternate track papers & posters, May 2004.
[13] Xing, W.; Ghorbani, A.; “Weighted PageRank algorithm;” Proceedings of the Second Annual Conference onCommunication Networks and Services Research, 19-21 May 2004; pp. 305 – 314.
[14] Dae-Young Choi ;”Enhancing the power of Web search engines by means of fuzzy query” Decision Support Systems,Volume 35, Issue 1, April 2003, pp. 31-44.
[15] Wen-Xue Tao; Wan-Li Zuo;” Query-sensitive self-adaptable web page ranking algorithm” Machine Learning andCybernetics, 2003 International Conference on Volume 1, 2-5 Nov. 2003 Page(s):413 - 418 Vol.1
[16] Ian H. Witten, Alistair Moffat, and Timothy C. Bell. Managing Gigabytes. Morgan KaufmannPublishers, San Francisco, 1999.
[17] Matthew Richardson and Pedro Domingos. The Intelligent Surfer: Probabilistic Combination ofLink and Content Information in PageRank, volume 14. MIT Press, Cambridge, MA, 2002.
[18] Davood Rafiei and Alberto O. Mendelzon. What is this page known for? Computing web pagereputations. In Proceedings of the Ninth International World Wide Web Conference, 2000.
[19] David Pennock, Gary Flake, Steve Lawrence, Eric Glover, and C. Lee Giles. Winner’s don’t take all: Characterizing the competition for links on the web. In Proceedings of the National Academyof Sciences, 2002.
[20] Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The PageRank citationranking: Bringing order to the web. Stanford Digital Libraries Working Paper, 1998.
[21] Rajeev Motwani and Prabhakar Raghavan. Randomized Algorithms. Cambridge University Press,United Kingdom, 1995.
[22] Tom Mitchell. Machine Learning, chapter 6, pages 177–184. McGraw-Hill, Boston, 1997.
[23] Andrew McCallum and Kamal Nigam. A comparison of event models for naive bayes textclassification. In AAAI-98 Workshop on Learning for Text Categorization, 1998.
[24] Jon Kleinberg. Authoritative sources in a hyperlinked environment. In Proceedings of the ACMSIAMSymposium on Discrete Algorithms, 1998.
[25] Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, and Gene H. Golub. Extrapolation methods for accelerating PageRank computations. In Proceedings of the TwelfthInternational World Wide Web Conference, May 2003.
[26] Glen Jeh and Jennifer Widom. Scaling personalized web search. In Proceedings of the TwelfthInternational World Wide Web Conference, May 2003
[27] J. Hirai, S. Raghavan, H. Garcia-Molina, and A. Paepcke. Webbase: A repository of web pages.In Proceedings of the Ninth International World Wide Web Conference, 2000.
[28] Fabrizio Angiulli, Senior Member, IEEE, Stefano Basta, Stefano Lodi, and Claudio Sartori “Distributed Strategies for Mining Outliersin Large Data Sets” IEEE transactions on knowledge and data engineering, vol. 25, no. 7, july 2013.
[29] Z. He, X. Xu and S. Deng, “Discovering Cluster-based Local Outliers”. Pattern Recognition Letters, Volume 24, Issue 9-10, pages 1641 – 1650, June 2003.
[30] S. Hawkins, H. He, G. Williams and R. Baxter, “Outlier Detection Using Replicator Neural Networks”. In Proceedings of the Fourth International Conference on Data Warehousing and Knowledge Discovery, pages 170 – 180, 2002.
[31] M. Halkidi, Y. Batistakis and M. Vazirgiannis, “Clustering Validity Checking Methods: Part II”. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Volume 31, Issue 3, pages19 – 27, September 2002.
[32] M. Halkidi, Y. Batistakis and M. Vazirgiannis, “Cluster Validity Methods: part I”. InProceedings of the ACM SIGMOD International Conference on Management of Data, Volume 31, Issue 2, pages 40 – 45, June 2002.
[33] M. Halkidi, Y. Batiskakis and M. Vazirgiannis, “Clustering algorithm and validity measures”. In Proceedings of the Thirteenth International Conference on Scientific and Statistical Database Management, pages 3 – 22, Fairfax, Virginia, USA, July, 2001.
[34] S. Guha, R. Rastogi and K. Shim, “ROCK: A Robust Clustering Algorithm for Categorical Attributes”. In Proceedings of the 15th International Conference on Data Engineering, page 512, March 1999.
[35] S. Giha, R. Rasstogi and K. Shim, “CURE: an efficient clustering algorithm for large databases”. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pages 73 – 84, June 1998.
[36] P. Fränti and J. Kivijärvi, “Randomised Local Search Algorithm for the Clustering Drawback”. Pattern Analysis and Applications, Volume 3, Issue 4, pages 358 – 369, 2000.
[37] A. Frome, Y. Singer, F. Sha, and J. Malik. Learning globallyconsistent local distance functions for shape-based image retrievaland classification. In ICCV, 2007.
[38] A. Frome, Y. Singer, and J. Malik. Image retrieval and recognitionusing local distance functions. In NIPS, 2006.
[39] P.-M. Cheung and J. T. Kwok. A regularization framework for multiple-instance learning. In ICML, 2006.
[40] B. Borah, D. K. Bhattacharyya, “An Improved Sampling-based DBSCAN for Large Spatial Databases”. In Proceedings of the International Conference on Intelligent Sensing and Information, page 92, 2004.
[41] C. Agrawal and P. Yu, “Redefining Clustering for High-Dimensional Applications”. In Proceedings of the IEEE International Conference on Transaction of Knowledge and Data Engineering, Volume 14, Issue 2, pages 210 – 225, April 2002.

Keywords
Ranking Optimization, Line up algorithm