Browse > Article
http://dx.doi.org/10.3745/JIPS.04.0075

Contribution to Improve Database Classification Algorithms for Multi-Database Mining  

Miloudi, Salim (Dept. of Computer Science, Faculty of Computer Science and Mathematics, University of Sciences and Technology-Mohamed Boudiaf (USTOMB))
Rahal, Sid Ahmed (Dept. of Computer Science, Faculty of Computer Science and Mathematics, University of Sciences and Technology-Mohamed Boudiaf (USTOMB))
Khiat, Salim (Dept. of Computer Science, Faculty of Computer Science and Mathematics, University of Sciences and Technology-Mohamed Boudiaf (USTOMB))
Publication Information
Journal of Information Processing Systems / v.14, no.3, 2018 , pp. 709-726 More about this Journal
Abstract
Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.
Keywords
Connected Components; Database Classification; Graph-Based Algorithm; Multi-Database Mining;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. Liu, H. Lu, and J. Yao, "Toward multi-database mining: identifying relevant databases," IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 4, pp. 541-553, 2001.   DOI
2 R. Agrawal and J. C. Shafer, "Parallel mining of association rules," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962-969, 1996.   DOI
3 R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases," in Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, 1994, pp. 487-499.
4 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, pp. 53-87, 2004.   DOI
5 A. Adhikari, P. Ramachandrarao, and W. Pedrycz, Developing Multi-database Mining Applications. London: Springer, 2010.
6 T. H. Cormen, C. E. Leiserson, and R. L. Rivest, Introduction to Algorithms. Cambridge, MA: MIT Press, 1990.
7 X. Wu, C. Zhang, and S. Zhang, "Database classification for multi-database mining," Information Systems, vol. 30, no. 1, pp. 71-88, 2005.   DOI
8 S. Zhang and M. J. Zaki, "Mining multiple data sources: local pattern analysis," Data Mining and Knowledge Discovery, vol. 12, no. 2-3, pp. 121-125, 2006.   DOI
9 S. Zhang, X. Wu, and C. Zhang, "Multi-database mining," IEEE Computational Intelligence Bulletin, vol. 2, no. 1, pp. 5-13, 2003.
10 S. Zhang, C. Zhang, and X. Wu, Knowledge Discovery in Multiple Databases. New York, NY: Springer, 2004.
11 H. Li, X. Hu, and Y. Zhang, "An improved database classification algorithm for multi-database mining," in Frontiers in Algorithmics. Heidelberg: Springer, 2009, pp. 346-357.
12 A. Adhikari and P. R. Rao, "Efficient clustering of databases induced by local patterns," Decision Support Systems, vol. 44, no. 4, pp. 925-943, 2008.   DOI
13 Y. Liu, D. Yuan, and Y. Cuan, "Completely clustering for multi-databases mining," Journal of Computational Information Systems, vol. 9, no. 16, pp. 6595-6602, 2013.
14 H. Liu, H. Lu, and J. Yao, "Identifying relevant databases for multidatabase mining," in Research and Development in Knowledge Discovery and Data Mining. Heidelberg: Springer, 1998, pp. 15-18.