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Novel Approach to Improve Apriori Algorithm using Transaction Reduction and Clustering Algorithm

Anil Vasoya Published in Advanced Computing

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014
Year of Publication: 2014
© 2014 by IJAIS Journal
Authors Anil Vasoya
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  1. Anil Vasoya. Article: Novel Approach to Improve Apriori Algorithm using Transaction Reduction and Clustering Algorithm. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014 ICWAC 2014(1):37-44, June 2014. BibTeX

    @article{key:article,
    	author = "Anil Vasoya",
    	title = "Article: Novel Approach to Improve Apriori Algorithm using Transaction Reduction and Clustering Algorithm",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014",
    	year = 2014,
    	volume = "ICWAC 2014",
    	number = 1,
    	pages = "37-44",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Now a day, Association rules mining algorithms used to increased turnover of any product based company. Therefore, many algorithms were proposed to determine frequent itemsets. This paper also proposes a novel algorithm, which is resulting from merging two existing algorithms (i. e. Partition with apriori and transaction reduction algorithm) to derived frequent item sets from large database. The experiments are conducted to find out frequent item sets on proposed algorithm and existing algorithms by applying different minimum support on different size of database. It shows that designed algorithm (pafi with apriori algorithm) takes very much less time as well as it gives better performance when there is a large dataset. Whereas with increase in dataset, Apriori and Transaction reduction algorithm gives poor performance as compared to PAFI with apriori and proposed algorithm. The implemented algorithm shows the better result in terms of time complexity. It also handle large database with efficiently than existing algorithms.

Reference

  1. Agrawal R, Imielinski T, Swami A, "Mining association rules between sets of items in large databases". In: Proc. of the l993ACM on Management of Data, Washington, D. C, May 1993. 207-216
  2. D. Kerana Hanirex, Dr. M. A. Dorai Rangaswamy:" Efficient algorithm for mining frequent item sets using clustering techniques. " In International Journal on Computer Science and Engineering Vol. 3 No. 3 Mar 2011. 1028-1032
  3. Feng WANG, Yong-hua LI:"Improved apriori based on matrix",IEEE 2008, 152-155.
  4. Han Jiawei, Kamber Miceline. Fan Ming, Meng Xiaofeng translation, "Data mining concepts and technologies". Beijing: Machinery Industry Press. 2001
  5. Margatet H. Dunham. Data Mining, Introductory and Advanced Topics: Upper Saddle River, New Jersey: Pearson Education Inc. ,2003.
  6. Chen Wenwei, "Data warehouse and data mining tutorial". Beijing: Tsinghua University Press. 2006
  7. Tong Qiang, Zhou Yuanchun, Wu Kaichao, Yan Baoping, " A quantitative association rules mining algorithm". Computer engineering. 2007, 33(10):34-35
  8. Zhu Yixia, Yao Liwen, Huang Shuiyuan, Huang Longjun, " A association rules mining algorithm based on matrix and trees". Computer science. 2006, 33(7):196-198
  9. Wael A. AlZoubi, Azuraliza Abu Bakar, Khairuddin Omar," Scalable and Efficient Method for Mining Association Rules", International Conference on Electrical Engineering and Informatics 2009.
  10. Wael Ahmad AlZoubi, Khairuddin Omar, Azuraliza Abu Bakar "An Efficient Mining of Transactional Data Using Graph-based Technique",3rd Conference on Data Mining and Optimization (DMO) 2011, Selangor, Malaysia

Keywords

PAFI, , clustering, Transaction reduction