Researches

   Seminar Title :

DATA MINING ALGORTHIM FOR FINDING FREQUENT PATTERNS

    References :

[1] Han, Jiawei, and Micheline Kamber. "Data mining: concepts and techniques." (2001).
[2]Mohamad karim Sohrabi,“frequent pattern mining using huge transactional databases.” In Amirkabir Univerity , 2014.
[3] Park, Jong Soo, Ming-Syan Chen, and Philip S. Yu. An effective hash-based algorithm for mining association rules. Vol. 24, no. 2. ACM, 1995.
[4]Cheung, David, and S. D. Lee. "Maintenance of Discovered Association Rules." Knowledge Discovery for Business Information Systems (2000): 173.
[5]Brin, Sergey, Rajeev Motwani, Jeffrey D. Ullman, and Shalom Tsur. "Dynamic itemset counting and implication rules for market basket data." In ACM SIGMOD Record, vol. 26, no. 2, pp. 255-264. ACM, 1997.
[6]Toivonen, Hannu. "Sampling large databases for association rules." In VLDB, vol. 96, pp. 134-145. 1996.
[7]Sarawagi, Sunita, Shiby Thomas, and Rakesh Agrawal. Integrating association rule mining with relational database systems: Alternatives and implications. Vol. 27, no. 2. ACM, 1998.
[8]Geerts, Floris, Bart Goethals, and Jan Van den Bussche. "A tight upper bound on the number of candidate patterns." In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, pp. 155-162. IEEE, 2001.
[9]Park, J. S., M. S. Chen, and P. S. Yu. "ªEfficient Parallel Mining for Association Rules." In º Proc. Fourth Int'l Conf. Information and Knowledge Management. 1995.
[10]Agrawal, Rakesh, and John C. Shafer. "Parallel mining of association rules." IEEE Transactions on Knowledge & Data Engineering 6 (1996): 962-969.
[11]Cheung, David W., Jiawei Han, Vincent T. Ng, Ada W. Fu, and Yongjian Fu. "A fast distributed algorithm for mining association rules." In Parallel and Distributed Information Systems, 1996., Fourth International Conference on, pp. 31-42. IEEE, 1996.
[12]Zaki, Mohammed J., Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. "Parallel algorithms for discovery of association rules." Data Mining and Knowledge Discovery 1, no. 4 (1997): 343-373.
[13]Jiang, Fan, and Carson Kai-Sang Leung. "A Business Intelligence Solution for Frequent Pattern Mining on Social Networks." In Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, pp. 789-796. IEEE, 2014.
[14]Shyur, Huan-Jyh, Chichang Jou, and Keng Chang. "A data mining approach to discovering reliable sequential patterns." Journal of Systems and Software 86, no. 8 (2013): 2196-2203.
[15]Nori, Fatemeh, Mahmood Deypir, and Mohamad Hadi Sadreddini. "A sliding window based algorithm for frequent closed itemset mining over data streams." Journal of Systems and Software 86, no. 3 (2013): 615-623.
[16]Gole, Sheela, and Bharat Tidke. "Frequent itemset mining for Big Data in social media using ClustBigFIM algorithm." In Pervasive Computing (ICPC), 2015 International Conference on, pp. 1-6. IEEE, 2015.
[17]Leung, Carson Kai-Sang, and Yaroslav Hayduk. "Mining frequent patterns from uncertain data with MapReduce for Big Data analytics." In Database Systems for Advanced Applications, pp. 440-455. Springer Berlin Heidelberg, 2013.
[18]Yun, Unil, Gangin Lee, and Keun Ho Ryu. "Mining maximal frequent patterns by considering weight conditions over data streams." Knowledge-Based Systems 55 (2014): 49-65.
[19]Baralis, Elena, Tania Cerquitelli, Silvia Chiusano, and Anais Grand. "P-Mine: Parallel itemset mining on large datasets." In Data Engineering Workshops (ICDEW), 2013 IEEE 29th International Conference on, pp. 266-271. IEEE, 2013.
[20]Rao, V. Chandra Shekhar, and P. Sammulal. "Survey on Sequential Pattern Mining Algorithms." International Journal of Computer Applications 76, no. 12 (2013): 24-31.

 

 

 

 

 

 

 

 

 

 

 


 
 
 
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