Commenced in January 2007
Paper Count: 31097
An Efficient Data Mining Approach on Compressed Transactions
Abstract:In an era of knowledge explosion, the growth of data increases rapidly day by day. Since data storage is a limited resource, how to reduce the data space in the process becomes a challenge issue. Data compression provides a good solution which can lower the required space. Data mining has many useful applications in recent years because it can help users discover interesting knowledge in large databases. However, existing compression algorithms are not appropriate for data mining. In [1, 2], two different approaches were proposed to compress databases and then perform the data mining process. However, they all lack the ability to decompress the data to their original state and improve the data mining performance. In this research a new approach called Mining Merged Transactions with the Quantification Table (M2TQT) was proposed to solve these problems. M2TQT uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate itemsets which are impossible to become frequent in order to improve the performance of mining association rules. The experiments show that M2TQT performs better than existing approaches.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080979Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1682
 M. C. Hung, S. Q. Weng, J. Wu, and D. L. Yang, "Efficient Mining of Association Rules Using Merged Transactions," in WSEAS Transactions on Computers, Issue 5, Vol. 5, pp. 916-923, 2006.
 M. Z. Ashrafi, D. Taniar, and K. Smith, "A Compress-Based Association Mining Algorithm for Large Dataset," in Proceedings of International Conference on Computational Science, pp. 978-987, 2003.
 U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "The KDD process for extracting useful knowledge from volumes of data," Communications of the ACM, Vol. 39, pp. 27-34, 1996.
 E. Hullermeier, "Possibilistic Induction in Decision-Tree Learning," in Proceedings of the 13th European Conference on Machine Learning, pp. 173-184, 2002.
 J. R. Quinlan, "C4.5: programs for machine learning," Morgan Kaufmann Publishers Inc, 1993.
 A. K. Jain and R. C. Dubes, Algorithm for clustering data: Prentice-Hall, Inc., 1988.
 R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules Between Sets of Items in Large Databases," in Proceedings of the International Conference on Management of Data, pp. 207-216, 1993.
 R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
 D. I. Lin and Z. M. Kedem, "Pincer-search: an efficient algorithm for discovering the maximum frequent set," IEEE Transactions on Knowledge and Data Engineering, Vol. 14, pp. 553-566, 2002.
 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, "Dynamic Itemset Counting and Implication Rules for Market Basket Data," in Proceedings of the International Conference on Management of Data, pp. 255-264, 1997.
 A. Savasere, E. Omiecinski, and S. Navathe, "An Efficient Algorithm for Mining Association Rules in Large Databases," in Proceedings of the 21st International Conference on Very Large Data Bases, pp. 432-444, 1995.
 J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," in Proceedings of the International Conference on Management of Data, pp. 1-12, 2000.
 D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu, "MAFIA: A maximal frequent itemset algorithm," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, pp. 1490-1504, 2005.
 G. Grahne and J. Zhu, "Fast algorithms for frequent itemset mining using FP-trees," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, pp. 1347-1362, 2005.
 IBM Almaden Research Center, "Synthetic Data Generation Code for Associations and Sequential Patterns," URL:http://www.almaden.ibm. com/software/quest/, 2006.
 D. W. L. Cheung, S. D. Lee, and B. Kao, "A general incremental technique for maintaining discovered association rules," in Proceedings of the 15th International Conference on Database Systems for Advanced Applications, pp. 185-194, 1997.
 D. Xin, J. Han, X. Yan, and H. Cheng, "Mining Compressed Frequent-Pattern Sets," in Proceedings of the 31st international conference on Very Large Data Bases, pp. 709-720, 2005.