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Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 109 - Number 8 |
| Year of Publication: 2015 |
| Authors: Alisha S. Patel, Mohit Patel |
10.5120/19212-1046
|
Alisha S. Patel, Mohit Patel . Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review. International Journal of Computer Applications. 109, 8 ( January 2015), 46-49. DOI=10.5120/19212-1046
Abstract
Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods. MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.
References
- Huang QingLan, Duan LongZhen, "Multi-level association rule mining based on clustering partition", IEEE(2013).
- Pratima Gautam, Dr. K. R. Pardasani , "Algorithm for Efficient Multilevel Association Rule Mining", IJCSE(2010).
- S. Prakash, M. Vijayakumar, " A Novel Method of Mining Association Rule with Multilevel Concept Hierarchy", IJCA(2011).
- YIN-BO WAN, YONG LIANG, LI-YA DING, "Mining Multilevel Association Rules With Dynamic Concept Hierarchy", IEEE(2008).
- Deepak Vidhate, Dr. Parag Kulkarni, "To improve Association Rule Mining using New Technique: Multilevel Relationship Algorithm towards Cooperative Learning" ,IEEE(2014).
- Virendra Kumar Shrivastava1, Parveen Kumar2, K. R. Pardasani3, "Discovery of Multi-level Association Rules from Primitive Level Frequent Patterns Tree", IJCSCT(2010).
- Younghee Kim, Ungmo Kim, "Mining Multilevel Association Rules on RFID data", IEEE (2009).
- Margaret H. Dunham, Yongqiao Xiao Le Gruenwald, Zahid Hossain, "A survey of association rules".
- Nikunj H. Domadiya, Udai Pratap Rao , "Hiding Sensitive Association Rules to Maintain Privacy and Data Quality in Database", IEEE(2012).
- Sonia Setia 1, Dr. Jyoti2, "Multi-Level Association Rule Mining: A Review", IJCTT(2013).
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