Total views : 170

Mining Frequent ItemSet Based on Clustering of Bit Vectors

Affiliations

  • Department of Computer Science, L. N. Government College, Ponneri - 601204, Tamil Nadu, India
  • Research Department of Computer Science, Presidency College, Chennai - 600005, Tamil Nadu, India
  • Department of Computer Science, K.C.G. College of Technology, Chennai - 600097, Tamil Nadu, India

Abstract


Objectives: In data mining, finding frequent item set from voluminous databases is an important role. It is a challenging task to find item sets frequently arise in the exponential growth of the databases. The number of scans to find the frequent item set is assessed as more and created redundancy. Methods/Statistical analysis: In this research work, a new technique is applied to improve the reduction in number of scans and minimize the redundancy. An algorithm is developed to mine the frequent item set using clustering techniques. A cluster table of frequent item set is created with minimal number of scanning. Findings: The support count is fixed to eliminate the duplicates which minimize the redundancy. This algorithm is appreciable, because the intermediate data in the dataset can be reused. The efficiency of algorithm can be identified by seeing the experimental results that is significantly performed well than the exiting algorithms. Applications/Improvements: In future, other algorithms are applied for the same data set to test its effectiveness in order to minimize the redundancy which avoids wastage of the memory space.

Keywords

Association Rule Mining, Bit Vector, Cluster, Frequent Itemset.

Full Text:

 |  (PDF views: 189)

References


  • Agarwal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of Data; 1993. p. 207–16.
  • Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York, ACM Press; 2000. p. 1–12.
  • Liu C, Cheng J. The software engineering school, China. Fast mining and updating frequent itemsets. ISECS International Colloquium on Computing, Communication, Control and Management. 2008; 1:365–8.
  • Dong J, Han M. BitTableFI: An efficient mining frequent itemsets algorithm. Journal of Elsevier on Knowledge-Based Systems. 2007; 20:329–35.
  • Yu H, Web J, Wang H, Jun L. An improved Apriori algorithm based on the Boolean Matrix and Hadoop; 2011.
  • Wur SH, Leu Y. An efficient Boolean Algorithm for mining association rules in large databases. 6th International Conference on Database Systems for Advanced Applications; 1999. p. 179–86.
  • Chao L, Zhao-Ping Y. Improved method of Apriori algorithm based on Matrix[j]. Proceedings of Computer Engineering of China. 2006; 23:68–9.
  • Tsay YJ, Chiang JY. CBAR: An efficient method for mining association rules. Knowledge-Based Systems. 2005; 18(2–3):99–105.
  • Alzoubi WA, Bakar AA, Omar K. Scalable and efficient method for mining association rules. International Conference on Electrical Engineering and Informatics; 2009 Aug. p. 5–7.
  • Krishnamurthy M, Kannan A, Baskaran R, Kavitha M. Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases. Journal of Elsevier on Procedia Computer Science. 2001; 3:513–23.
  • Krishnamurthy M, Manivannan K, Chilambuchelvan A, Rajalakshmi E, Kannan A. Enhanced candidate generation for frequent item set generation. Indian Journal of Science and Technology. 2015 Jul; 8(13). DOI: 10.17485/ijst/2015/v8i13/60756.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.