The week's pick
Reseach Article
A Novel Algorithm for Mining Hybrid-Dimensional Association Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 16 |
| Year of Publication: 2010 |
| Authors: R.Chithra, S.Nickolas |
10.5120/342-521
|
R.Chithra, S.Nickolas . A Novel Algorithm for Mining Hybrid-Dimensional Association Rules. International Journal of Computer Applications. 1, 16 ( February 2010), 53-59. DOI=10.5120/342-521
Abstract
The important issue for association rules generation is the discovery of frequent itemset in data mining. Most of the existing real time transactional databases are multidimensional in nature. The classical Apriori algorithm mainly concerned with handling single level, single-dimensional boolean association rules. These algorithms scan the transactional databases or datasets many times to find frequent itemsets. This paper considers mining hybrid-dimensional association rules, from transactional database, which is very interesting and useful in real life business decision making. A novel algorithm is proposed for mining hybrid-dimensional association rules using multi index-structures for storing multidimensional interdimensional and intradimensional frequent-itemset, and it stores all frequent 1-itemsets after scanning the entire database first time in the temporary table for compression of the transactional dataset. From then, k-itemsets are generated with no further scan of the datasets. Compared to traditional algorithms, this algorithm efficiently finds association rules in multidimensional datasets, by scanning the dataset only once, thus enhancing the process of data mining.
References
- Agrawal, R., Imielinski, T., Swami, A., 1993. Mining Association rules between sets of items in large databases. In. Proceedings of ACM-SIGMOD, pp. 206-216
- Agrawal, R. and Srikant,R. 1994. Fast algorithms for mining association rules. In Proceedings of International Conference on Very Large Data Bases (VLDB '94), pp. 487-499
- Agrawal, R. and Srikant,R. 1995. Mining Sequential Patterns, In Proceedings of IEEE International Conference on Data Engineering, pp. 3-14.
- Anthony J.T Lee, Wan-chuen Lin, Chun-Sheng Wang , 2006. Mining association rules with multi-dimensional constraints. Elsevier, The Journal of Systems and Software 79, pp.79-92
- Chuan Li, Tang, Yu, Zhang, Liu, Zhu, Jiang 2006. Mining Multi-dimensional frequent Pattern Without Data Cube Construction. Springer-Verlag Berlin Heidelberg 2006, LNAI 4099, pp. 251-260
- Chung-Ching Yu and Yen-Liang Chen, 2008. Mining Sequential Patterns from Multidimensional Sequence Data. IEEE Transactions on Knowledge and Data Engineering, VOL. 17, NO. 1. Pp. 136-140
- Jiawei Han, Micheline Chamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, Hardcover, ISBN 1558604898
- Jiawei Han, Jian Pei , Yiwen Yin, Runying Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Patterns Tree Approach, Data Mining and Knowledge Discovery, 8, 53-87, 2004, Kluwer Academic Publishers
- Jiawei Han , Hong Cheng, Dong Xin, Xifeng Yan, 2007. Frequent pattern mining: current status and future directions Springer Science+Business Media, LLC, Data Mining Knowl Disc (2007) 15:55-86
- Mannila, H., Toivonen, H., Verkamo, A.I., 1994. Efficient Algorithm for Discovering Association Rules. In Proceedings of AAAI'94 Workshop Knowledge Discovery in Databases, pp. 181-192
- Ng, R., Lakshmanan, L.V.S., Han, J., Pang, A., 1998. Exploring Mining and Pruning optimization of constrained Association Rules. In Proceedings ACM-SIGMOD. Intrenational Conference on Management of Data, pp. 13-24
- Runying Mao, 2001. Adaptive-FP: An efficient and Effective method for multi-level multi-dimensional Frequent Pattern, Master of Science Thesis, Simon Fraser University
- Srikant, R., Vu, Q., and Agrawal, R. 1997. Mining association rules with item constraints. In Proc. 1997 Int. Conference on Knowledge Discovery and Data Mining, pp. 67-73.
- Tongyuan Wang, Huzhan Zheng, Yanjiang Qiao 2007, An Interactive Hyper Knowledge Discovery System for Chinese Medicine IEEE Fourth International conference on Fuzzy Systems and Knowledge Discovery.
- WanXin Xu, RuJing Wang, 2006. A Novel Algorithm of mining Multidimensional Association Rules. Springer- Verlag, LNCIS 344, pp. 771-60
- Yan Xin, Shi-Guang ju , 2003. Mining Conditional Hybrid- Dimensional Association Rules on the basis of Multi- dimensional Transaction Database. In Proc. Second Int. Conf. Machine Learning and Cybernetics, PP. 216-221
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