The week's pick
Random Articles
Reseach Article
A Matrix based Maximal Frequent Itemset Mining Algorithm without Subset Creation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 159 - Number 6 |
| Year of Publication: 2017 |
| Authors: Balwant Kumar, Dharmender Kumar |
10.5120/ijca2017912963
|
Balwant Kumar, Dharmender Kumar . A Matrix based Maximal Frequent Itemset Mining Algorithm without Subset Creation. International Journal of Computer Applications. 159, 6 ( Feb 2017), 23-26. DOI=10.5120/ijca2017912963
Abstract
Frequent pattern mining is main step in association rule mining. Several algorithms have been proposed for this, but the majority of these algorithms have two main problems that is large number of database scan and generating large candidate itemsets. This process is time intense because these algorithms first mine the minimal frequent itemsets and then generate maximal frequent itemsets from minimal frequent itemsets. Present paper proposes a new top down approach based on compressed matrix for mining maximal frequent itemsets directly without the help of subset. The proposed algorithm performs better than Maximal Frequent Itemset First (MFIF) algorithms with datasets of different size and on different threshold.
References
- R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. of Int. Conf. Very Large Data Bases (VLDB’94), Santiago, Chile, pp: 487–499, Sept. 1994.
- Sukhbir Singh, Dharmender Kumar “Frequent Pattern Mining Algorithms: A Review”, International Journal of Advanced Engineering and Nano Technology, Vol -1, No -10, 2014, pp 1-7.
- Bayardo RJ. “Efficiently Mining Long Patterns from Databases,” in Proceedings ACM SIGMOD International Conference on Management of Data, pp.85–93, 1998.
- Lin DI, Kedem ZM. “Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set,” in Proceedings of 6th International Conference on Extending Database Technology, pp.105–119, 1998.
- Agarwal RC, Aggarwal CC, Prasad VVV. “Depth First Generation of Long Patterns,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.108–118,2000.
- Burdick, D., Calimlim,M., and Gehrke, J. “MAFIA: A maximal frequent itemset algorithm for transactional databases,” in Proceedings of IEEE International Conference on Data Engineering, pp. 443–452, 2001.
- K. Gouda and M. J. Zaki. Efficiently Mining Maximal Frequent Itemsets. Proc. of the IEEE Int. Conference on Data Mining, San Jose, 2001.
- QinghuaZou, Wesley W. Chu and Baojing Lu, “SmartMiner: A depth first algorithm guided by tail information for mining maximal frequent itemsets,” in Proceedings of the IEEE International Conference on Data Mining. pp.570 – 577, 2002.
- Don-Lin Yang, Ching-Ting Pan and Yeh-Ching Chung , "An efficient hash-based method for discovering the maximal frequent set," 25th Annual International Conference on Computer Software and Applications, pp.511-516, 2001.
- A.M.J. Md. ZubairRahman, P. Balasubramanieand P. VenkataKrihsna, “A Hash based MiningAlgorithm for Maximal Frequent Itemsets using Linear Probing,”Infocomp Journal of Computer Science, Vol.8, No.1,pp.14-19, 2009.
- M. Krishnamurthy, A. Kannan , R. Baskaran, R. Deepalakshmi “Frequent Item set Generation Using Hashing-Quadratic Probing Technique,” European Journal of Scientific Research Vol.50, No.4, pp. 523-532, 2011.
- Jnanamurthy HK, Vishesh HV, Vishruth Jain, Preetham Kumar, Radhika M. Pai, “Discovery of Maximal Frequent Item Sets using Subset Creation,”International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.1, Jan. 2013.
Index Terms
Keywords