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Ranking Optimization using Outlier Detection Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 123 - Number 3 |
| Year of Publication: 2015 |
| Authors: Yogendra Mahate, Roopam Gupta, Sanjeev Sharma |
10.5120/ijca2015905257
|
Yogendra Mahate, Roopam Gupta, Sanjeev Sharma . Ranking Optimization using Outlier Detection Technique. International Journal of Computer Applications. 123, 3 ( August 2015), 11-14. DOI=10.5120/ijca2015905257
Abstract
Product ranking optimization is an emerging required research area where we are getting a heavy duly competition day by day, number of products with sort of ranges are available in market, finding the best out of the different product is a major problem, several times it observed that number of survey has been made in order to make a product ranking and show it to users about the best available product for their requirement in the market, the ranking been held on multiple required attributes work or play the role to increase or decrease their ranking. Existing Line-up technique is only provided rank to number of products. It did not optimize the ranking. Now, in this paper, defeat these entire problems and provide the best solution. Introduce the outlier detection technique to optimize the product ranking according to requirement feature or attribute. Here i am going to implement some algorithm to detect best outliers such as distributing solving set algorithm, line-up algorithm & improved LAZY DSS.
References
- E. Hung and D.W. Cheung, “Parallel Mining of Outliers in Large Database,” Distributed and Parallel Databases, vol. 12, 2002.
- E. Knorr and R. Ng, “Algorithms for Mining Distance-Based Outliers in Large Datasets,” Proc. 24rd Int’l Conf. Very Large Data Bases (VLDB), pp. 392-403, 1997.
- E. Lozano and E. Acun ˜a, “Parallel Algorithms for Distance-Based and Density-Based Outliers,” Proc. Fifth IEEE Int’l Conf. Data Mining (ICDM), pp. 729-732, 2005.
- S.D. Bay and M. Schwabacher, “Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule,” Proc. Ninth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2003.
- M.E. Otey, A. Ghoting, and S. Parthasarathy, “Fast Distributed Outlier Detection in Mixed-Attribute Data Sets,” Data Mining Knowledge Discovery, vol. 12, nos. 2/3, pp. 203-227, 2004.
- A. Koufakou and M. Georgiopoulos, “A Fast Outlier Detection Strategy for Distributed High-Dimensional Data Sets with Mixed Attributes,” Data Mining Knowledge Discovery, vol. 20, pp. 259-279, 2009.
- H. Dutta, C. Giannella, K.D. Borne, and H. Kargupta, “Distributed Top-K Outlier Detection from Astronomy Catalogs Using the DEMAC System,” Proc. SIAM Int’l Conf. Data Mining (SDM), 2007.
- S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets,” Proc. Int’l Conf. Managment of Data (SIGMOD ’00), pp. 427-438, 2000.
- S.D. Bay and M. Schwabacher, “Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule,” Proc. Int’l Conf. Knowledge Discovery and Data Mining (KDD ’03), 2003
- E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, “A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data,” Applications of Data Mining in Computer Security, Kluwer, 2002.
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