Abstract
Problem of finding frequent patterns has long been studied because it is very essential to data mining tasks such as association rule analysis, clustering, and classification analysis. Privacy preserving data mining is another important issue for this domain since most users do not want their private information to leak out. In this paper, we proposed an efficient approach for mining maximal frequent patterns from a large transactional database with privacy preserving capability. As for privacy preserving, we utilized prime number based data transformation method. We also developed a noble algorithm for mining maximal frequent patterns based on lattice structure. Extensive performance analysis shows the effectiveness of our approach.
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Wang, J.L., Xu, C.F., Pan, Y.H.: An Algorithm for Mining Privacy-Preserving Frequent Itemsets. In: International Conference on Machine Learning and Cybernetics (2006)
Mustapha, N., Hossein, M., Shahraki, N., Mamat, A.B., Sulaiman, M.N.B.: A Numerical Method for Frequent Patterns Mining. Journal of Theoretical and Applied Information Technology
Fu, A.W.C., Wang, K.: Privacy-Preserving Frequent Pattern Mining Across Private Databases. In: Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM 2005 (2005)
Oliveira, S.R.M., Zane, O.R.: Privacy Preserving Frequent Itemset Mining. In: IEEE International Conference on Data Mining Workshop on Privacy, Security, Maebashi City, Japan
Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering (2000)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceeding of the 20th Inl. Conf. on Very Large Databases (VLDB), Santiago, Chile, pp. 487–499 (1994)
Gouda, K., Zaki, M.J.: Efficiently Mining Maximal Frequent Itemsets. In: Proc. of the 1st IEEE International Conf. on Data Mining, San Jose, USA, pp. 163–170 (2001)
Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases. In: Proceedings of the 17th Inl. Conf. on Data Engineering, Germany (2001)
Gouda, K., Zaki, M.: A New Method for Mining Maximal Frequent Itemsets. In: Proceedings of the World Congress on Engineering, London, U.K (2008)
Burdick, D., Calimlim, M., Gehrke, J.: GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets. In: Data Mining and Knowledge Discovery, The Netherlands (2005)
Mustapha, N., Sulaiman, M.N., Othman, M., Selamat, M.H.: Fast Discovery of Long Patterns for Association Rules. International Journal of Computer Mathematics (2003)
Shrivastava, R., Awasthy, R., Solanki, B.: New Improved Algorithm for Mining Privacy Preserving Frequent Itemsets. International Journal of Computer Science & Informatics 1 (2011)
http://empslocal.ex.ac.uk/people/staff/mrwatkin/zeta/tutorial.htm
Wang, H., Hu, C., Chen, Y.: Mining Maximal Patterns Based on Improved FP-lattice structure and Array Technique. In: 2nd International Conference on Future Computer and Communication (2010)
Leung, C.K., Khan, Q.I., Hoque, T.: CanTree: A canonical-order tree structure for frequent pattern mining. Knowledge and Information Systems (2007)
Kai, Y., Yuan, M.: A Fast Algorithm For Discovering Maximum Frequent Item sets. In: IEEE 3rd International Conference on Communication Software and Networks, ICCSN (2011)
Rymon, R.: Search through systematic set enumeration. In: Proc. 3rd Int’l Conf. on PKRR
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Karim, M.R., Rashid, M.M., Jeong, BS., Choi, HJ. (2012). Privacy Preserving Mining Maximal Frequent Patterns in Transactional Databases. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_23
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DOI: https://doi.org/10.1007/978-3-642-29038-1_23
Publisher Name: Springer, Berlin, Heidelberg
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