The week's pick
Reseach Article
Optimization of Horizontal Aggregation in SQL by using C4.5 Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 93 - Number 13 |
| Year of Publication: 2014 |
| Authors: Priti Phalak, Rekha Sharma |
10.5120/16277-6048
|
Priti Phalak, Rekha Sharma . Optimization of Horizontal Aggregation in SQL by using C4.5 Algorithm. International Journal of Computer Applications. 93, 13 ( May 2014), 31-37. DOI=10.5120/16277-6048
Abstract
For efficient analysis of some data mining system and algorithms, data is required in the horizontal aggregated format. In a relational database, datasets are highly normalized and major efforts are required to compute aggregation when they are expected in horizontal form which is suitable for some data mining, statistical and machine learning algorithm. Query optimization techniques used for vertical (standard) aggregation is not suitable for horizontal aggregation. That's why we propose an optimization technique for horizontal aggregation. To optimize horizontal aggregation we are using C4. 5 classification algorithm and query evaluation methods. Horizontal Aggregation represents a template to generate SQL code which automates writing SQL queries, optimizing them, and testing them for correctness. It also reduces manual work in the data preparation phase in a data mining. There are various applications where the horizontal aggregation is used such as electrical billing, banks, hospital management system, pharmacy and online library etc.
References
- C. C. Ordonez, and Zhibo Chen, "Horizontal Aggregation in SQL to prepare Data Sets for Data Mining Analysis," IEEE Transactions on Knowledge and Data Engineering (TKDE), April 2012.
- C. Cunningham, G. Graefe, and C. A. Galindo-Legaria, "PIVOT and UNPIVOT: Optimization and Execution Strategies in an RDBMS", Proc. 13th Int'l Conf. Very Large Data Bases (VLDB '04), pp. 998-1009, 2004.
- Venkatadri. m, Lokanatha C. Reddy"A Comparative Study On Decision Tree Classification Algorithms In Data Mining" ISSN: 0974-3596, April '10 – Sept '10, Volume 2 : Issue 2, Page: 24.
- R. Rakesh Kumar, A. Bhanu Prasad," K Means Clustering Algorithm for Partitioning Data Sets Evaluated From Horizontal Aggregations", IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 12, Issue 5 (Jul. - Aug. 2013), PP 45-48.
- Joyce Jackson, Data Mining: A Conceptual Overview. Communications of the Association for Information Systems (Volume 8, 2002) 267-296.
- Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yangb,Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinbergand, "Top 10 algorithms in data mining", Received: 9 July 2007 / Revised: 28 September 2007 / Accepted: 8 October 2007 Published online: 4 December 2007 © Springer-Verlag London Limited 2007.
- J. R. Quinlan, "C4. 5: Programs For Machine Learning". Morgan Kaufmann Los Altos, 1993.
- Matthew N. Anyanwu, Sajjan G. Shiva , "Comparative Analysis of Serial Decision Tree Classification Algorithms", International Journal of Computer Science and Security, (IJCSS) Volume (3) : Issue (3).
- Anju Rathee, Robin prakash mathur, "Survey on Decision Tree Classification algorithms for the Evaluation of Student Performance", International Journal of Computers & TechnologyVolume 4 No. 2, March-April, 2013, ISSN 2277-3061.
- Durka. C, Kerana Hanirex. D,"An Efficient Approach for Building Dataset in Data Mining", IJARCSSE, Volume 3, Issue 3, March 2013. .
Index Terms
Keywords