The week's pick
Random Articles
Reseach Article
An Efficient Technique for Indexing Temporal Databases
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 127 - Number 13 |
| Year of Publication: 2015 |
| Authors: Mohammed M. Fouad, Mostafa G.M. Mostafa |
10.5120/ijca2015906590
|
Mohammed M. Fouad, Mostafa G.M. Mostafa . An Efficient Technique for Indexing Temporal Databases. International Journal of Computer Applications. 127, 13 ( October 2015), 32-37. DOI=10.5120/ijca2015906590
Abstract
Temporal databases added a new dimension to traditional transaction databases. This dimension is the life time of each item, i.e. exhibition period, starting from the partition when this item appears in the transaction database to the partition when this item no longer exists. Mining temporal association rules became very interesting topic in many applications nowadays. In this paper, an efficient technique is proposed for indexing temporal databases in order to facilitate support counting process during mining operation. Some experiments were conducted using well-known real datasets to show the performance of the proposed indexing technique with respect to index size and running time of the mining algorithm. The results show that the proposed indexing technique saves a lot of running time and works efficiently with different databases characteristics.
References
- Q. Zhao and S. Bhowmick, “Association Rule Mining: A Survey,” Technical Report, Center for Advanced Information Systems (CAIS), Nanyang Technological University, Singapore, 2003.
- R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. Int. Conf. Very Large Data Bases VLDB’94, 1994, pp. 487–499.
- J. Han, J. Pei, Y. Yin and R. Mao, “Mining frequent patterns without candidate generation: a frequent-pattern tree approach,” Data Mining and Knowledge Discovery, vol. 8, no. 1, 2004, pp. 53–87.
- G. Grahne and J. Zhu, “Fast algorithms for frequent itemset mining using FP-trees,” IEEE Trans. on Knowledge and Data Eng., vol. 17, no. 10, 2005, pp. 1347–1362.
- B. Vo, F. Coenen and B. Le, “A new method for mining Frequent Weighted Itemsets based on WIT-trees,” Expert Systems with Applications, vol. 40, no. 4, 2013, pp.1256-1264.
- H. Ning, H.Yuan and S. Chen, “Temporal association rules in mining method,” The 1st International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06), Zhejiang, China, vol. 2, 2006, pp. 739–742.
- C-Y. Chang, M-S. Chen and C-H. Lee, “Mining general temporal association rules for items with different exhibition periods,” in Proc. IEEE Int. Conf. Data Mining, 2002, pp. 59–66.
- C-H. Lee, C-R. Lin and M-S. Chen, “On Mining General Temporal Association Rules in a Publication Database,” in Proc. IEEE Int. Conf. Data Mining, 2001, pp. 487–499.
- C-H. Lee, M-S. Chen and C-R. Lin, “Progressive partition miner an efficient algorithm for mining general temporal association rules,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 4, 2003, pp. 1004–1017.
- J-W. Huang, B-R. Dai and M-S. Chen, “Twain Two-end association miner with precise frequent exhibition periods,” ACM Trans. on Knowledge Discovery from Data (TKDD), vo. 1, no. 2, 2007.
- Z. Linag, T. Ximming, L. Lin and J. Wenliang, “Temporal Association Rule Mining Based on T-Apriori Algorithm and its Typical Application,” International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion , 2005
- N. Khairudin, A. Mustapha and M. Ahmad, “Effect of Temporal Relationships in Associative Rule Mining for Web Log Data,” The Scientific World Journal, vol. 2014, Article ID 813983, 10 pages, 2014.
- Frequent Itemset Mining Dataset Repository (Accessed 1 Spetember 2015). Available online at: http://fimi.ua.ac.be/data/
Index Terms
Keywords