A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

Abstract:

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

• Periodical: Applied Mechanics and Materials (Volume 145)
• Pages: 292-296
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.145.292
• Online since: December 2011
• Authors: Lee Wen Huang
• Keywords: Closed Itemsets, Data Mining (DM), Graphic, Hashing

References

[1] R. Agrawal and R. Srikant, ``Fast Algorithms for Mining Association Rules, Proc. of the 20th Int. Conf. on Very Large Data Bases, pp.487-499, (1994).

[2] R. Agrawal and R. Srikant, ``Mining Sequential Patterns, Proc. of the 11th Int. Conf. on Data Engineering, pp.3-14, (1995).

[3] J. Han, H. Cheng, D. Xin, and X. Yan, ``Frequent Pattern Mining: Current Status and Future Directions, Data Mining and Knowledge Discovery, Vol. 15, No. 1, pp.55-86, Aug. (2007).

DOI: 10.1007/s10618-006-0059-1

[4] L. W. Huang and Y. I. Chang, ``An Efficient Graph-Based Approach to Mining Association Rules for Large Databases, International Journal of Intelligent Information and Database Systems, Vol. 3, No. 3, pp.274-259, (2009).

DOI: 10.1504/ijiids.2009.027686

[5] C. Kamath, ``The Role of Parallel and Distributed Processing in Data Mining, Tech. Rep. UCRL-JC-142468, Newsletter of the IEEE Technical Committee on Distributed Processing, (2001).

[6] C. Lucchese, S. Orlando, and R. Perego, ``Fast and Memory Efficient Mining of Frequent Closed Itemsets, IEEE Trans. on Knowledge and Data Engineering, Vol. 18, No. 1, pp.21-36, Jan. (2006).

DOI: 10.1109/tkde.2006.10

[7] C. Lucchese, S. Orlando, and R. Perego, `` Parallel Mining of Frequent Closed Patterns: Harnessing Modern Computer Architectures, ICDM, pp.1-10, (2007).

DOI: 10.1109/icdm.2007.13

[8] Y. Ohsawa, and K. Yada, ``Data Mining for Design and Marketing, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, (2009).

DOI: 10.1201/9781420070224.axa

[9] R. Srikant and R. Agrawal, ``Mining Sequential Patterns: Generalizations and Performance Improvements, Proc. of the 5th Int. Conf. on Extending Database Technology, pp.3-17, (1996).

DOI: 10.1007/bfb0014140

[10] J. M. Wei, W. G. Yi, and M. Y. Wang, ``Novel Measurement for Mining Effective Association Rules, Knowledge-Based Systems, Vol. 19, No. 8, pp.739-743, Dec. (2006).

DOI: 10.1016/j.knosys.2006.05.011

[11] M. A. Weiss, Data Structure and Algorithm Analysis in C++. Addison Wesley, (1993).