The week's pick
Reseach Article
A Novel Pattern Merger Algorithm for generating Actionable Rules for Multi-Source Combined Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 125 - Number 15 |
| Year of Publication: 2015 |
| Authors: Arti Deshpande, Anjali Mahajan |
10.5120/ijca2015906108
|
Arti Deshpande, Anjali Mahajan . A Novel Pattern Merger Algorithm for generating Actionable Rules for Multi-Source Combined Mining. International Journal of Computer Applications. 125, 15 ( September 2015), 17-21. DOI=10.5120/ijca2015906108
Abstract
The idea of combined mining is very useful and flexible for identifying in-depth patterns through combining sets of items from multiple datasets or using multiple data mining techniques. The identified combined patterns disclose in-depth business intelligence, which are more informative and actionable for business decision-making. The business data is scattered at various locations and to arrive at decisions it needs analysis of data by integrating entire data. This paper emphasizes on applying Association Rule Mining technique on various data sources located at different locations and patterns so obtained are transported to main site. Finally by applying the proposed novel Pattern Merger Algorithm on the aforesaid patterns, all the generated patterns are merged to obtain actionable rules. These actionable patterns assist in strategic business planning and also pin points various issues arising post application of the data integration technique. Domain Knowledge concept is also included in the Rule generation technique to obtain the final results which are in the form of actionable rules.
References
- Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao, " Domain Driven Data mining ," In Springer ISBN 978-1-4419-5736-8 , science+business media, LLC 2010
- Longbing Cao, Huaifeng Zhang Yanchang Zhao, Dan Luo, and Chengqi Zhang, "Combined Mining : Discovering Informative Knowledge in Complex Data, " In IEEE Trans Systems, Man, And Cybernetics, Vol. 41, No. 3, June 2011pp-699-712.
- Paul O’ Dea, Josephine Griffith, Colm O’ Riordan, “Combining Feature Selection and Neural Networks for Solving Classification , ”In Article 7/2001 CiteSeer
- Jia Hu, NingZhong, “Organizing Multiple Data Sources for Developing Intelligent e-Business Portals," In Springer Science + Business Media, Inc. Manufactured in the United States ,Data Mining and Knowledge Discovery, 12, 127-150, 2006
- Chuen-He Liou, "Improve the Quality of Product Recommendation based on Multi-channel CRM for E-commerce," In International Conference on Data Mining, DMIN'13
- Xiaoxin Yin and Jiawei Han, "Efficient Classification from Multiple Heterogeneous Databases," In Knowledge Discovery in Databases PKDD 2005, 2005 - Springer
- Arti Deshpande and Anjali Mahajan , "Domain Driven Multi-Feature Combined Mining for retail dataset, "InInternational Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 - 8958, Volume-2, Issue-3 February 2013
- Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Chengqi Zhang, and Hans Bohlscheid, "Combined Pattern Mining: From Learned Rules to Actionable Knowledge , " In Lecture Notes of Computer Science, 2008, Volume 5360/2008, pp. 393-403
- Arti Deshpande and Anjali Mahajan, "Serial Multimethod Combined Mining," In ICACCI, 2014 International Conference on Advances in Computing, Communications and Informatics 978-1-4799-3080-7/14, IEEE
- http://www.red-gate.com/products/sql-development/sql-data-generator/
- Mohammed Al-Maolegiand BassamArkok, "An Improved Apriori Algorithm for Association Rules," In International Journal on Natural Language Computing (IJNLC) Vol. 3, No.1, February 2014
- Charls X. Ling and Qiang Yang, “Discovering Classification from Data of Multiple Sources,” In Data Mining and Knowledge Discovery, 12, 181–201, 2006.
- Sayyadian, Mayssam. "HeteroClass: a framework for effective classification from heterogeneous databases." CS512 Project Report, University of Wisconsin, Madison (2006).
Index Terms
Keywords