The week's pick
Random Articles
Reseach Article
Memory Cutback for FP-Tree Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 89 - Number 12 |
| Year of Publication: 2014 |
| Authors: D. P. Rana, N. J. Mistry, M. M. Raghuwanshi |
10.5120/15682-4485
|
D. P. Rana, N. J. Mistry, M. M. Raghuwanshi . Memory Cutback for FP-Tree Approach. International Journal of Computer Applications. 89, 12 ( March 2014), 18-22. DOI=10.5120/15682-4485
Abstract
The pattern growth approach of association rule mining is very efficient as avoiding the candidate generation step which was utilized in Apriori algorithm. Here, revisited of the pattern growth approaches are done to improve the performance using different criteria like item search order, conditional database representation and construction approach and tree traversal ways. The header table construction is the first part in almost all the approaches having constant number of dataset items. This research is representing the reduction in overall memory requirement of pattern growth approach by reducing the search space and processor operations time at the header table generation. It is proposed to achieve the memory cutback by only considering the items that are going to be frequent and ignoring the infrequent items at early stage of scan, by considering the boundary. Experimental analysis achieves cutback in memory consumption in the proposed approach Modified FP-Growth (MFP-Growth) compare to FP-Growth and CFP-Growth.
References
- J. Han and M. Kamber, "Data Mining Concepts and Techniques", Second Edition, Morgan Kaufmann Publishers, 2006.
- R. Agrawal, T. Imielinski and Swami, "Mining association rules between sets of items in large databases," In Proceeding of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216, 1993.
- R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," Proceedings of 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
- J. Han, J. Pei and Y. Yin, "Minining frequent patterns without candidate generation," Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 53-87, 2000.
- Pietracaprina and D. Zandolin, "Mining frequent itemsets using patricia tries", In B. Goethals and M. J. Zaki, editors, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Melbourne, FL, USA, November 2003.
- F. C. Tseng and C. C. Hsu, "Generating frequent patterns with the frequent pattern list," Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 376-386, 2001.
- K. Wang, L. Tang, J. Han and J. Liu, "Top Down FP-Growth for Association Rule Mining", PAKDD 2002, LNAI 2336, Springer-Verlag Berlin, pp. 334-340, 2002.
- M. El-Hajj and O. R. Za?ane, "COFI-tree Mining: A New Approach to Pattern Growth with Reduced Candidacy Generation", In Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, USA, CEUR Workshop Proceedings, vol. 90, pp. 1-10, 19 December 2003.
- G. Liu. , H. Lu, J. Xu Yu, W. Wang and X. Xiao, "AFOPT: An Efficient Implementation of Pattern Growth Approach*", In Proceedings of IEEE ICDM'03 Workshop, FIMI'03, Melbourne, pp. 1-10, 2003.
- Y. G. Sucahyo and R. P. Gopalan, "CT-PRO: A Bottom Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure", In proceedings of IEEE ICDM Workshop on Frequent Itemset Mining Implementation (FIMI), Brighton UK, pp. 1-11, 2004.
- S. Xuequn, S. Kai-Uwe and G. Ingolf, "SQL Based Frequent Pattern Mining with FP-Growth", In Proceedings of 15th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2004, and 18th Workshop on Logic Programming, WLP, Germany, pp. 32-46, March 4-6, 2004.
- T. Hong, C. Lin and Y. Wu, "A fast updated frequent pattern tree", 2006 IEEE International Conference on Systems, Man and Cybernetics, Taiwan, 2006.
- Y. Hu and Y. Chen, "Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism", Decision Support Systems, Vol. 42(1), pp. 1-24, 2006.
- S. K. Tanbeer, C. F. Ahmed, B. Jeong and Y. Lee, "CP-Tree: A Tree Structure for Single-Pass Frequent Pattern Mining", PAKDD 2008, Springer LNAI 5012 - Verlag Berlin Heidelberg 2008, pp. 1022-1027, 2008.
- G. Pradeepini and S. Jyothi, "Tree-Based Incremental Association Rule Mining without Candidate Itemset Generation", In Trendz in Information Science & Computing (TISC), Chennai, pp. 78-81, Dec, 2010.
- K. Lin, I. Liao and Z. Chen, "An improved frequent pattern growth method for mining association rules", Expert Systems with Applications, Vol. 38, pp. 5154-5161, 2011.
- SPMF, A Sequential Pattern Mining Framework Available at: http://www. philippe-fournier-viger. com/spmf/index. php?link=datasets. php
Index Terms
Keywords