A Web Data Mining Algorithm Based on Weighted Association Rules

Abstract:

Association rules mining is attracting much attention in research community due to its broad applications. Existing web data mining methods suffer the problems that 1) the large number of candidate itemsets, which are hard to be pruned, should be pruned in advance. 2) the time of scanning the database, which are needed to scan transactional database repeatedly, should be reduced. In this paper, a new association rules mining model is introduced for overcoming above two problems. We develop an efficient algorithm-WARDM(Weighted Association Rules Data Mining) for mining the candidate itemsets. The algorithm discusses the generation of candidate-1 itemset, candidate-2 itemset and candidate-k itemset(k>2),which can avoid missing weighted frequent itemsets. And the transactional database are scanned only once and candidate itemsets are pruned twice, which can reduce the amount of candidate itemsets. Theoretical analysis and experimental results show the space and time complexity is relatively good, Meanwhile the algorithm decreases the number of candidate itemsets, enhances the execution efficiency.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 1386-1391
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.1386
• Online since: February 2011
• Authors: Xiao Lv, Yong Jie Li, Xu Lu
• Keywords: New-Apriori, WARDM, Web Data Mining, Weighted Association Rules

References

[1] Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth. Knowledge discovery and data mining: towards a unifying framework. In proc. of the 2nd int'1 corf. on knowledge discovery and data mining, 2007: 505-509PR.W. Thomas, L. A. Dasilva, and A.B. MacKenzie. Cognitive Networks. Proc. of the First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), 2005, pp.352-360.

DOI: 10.1109/dyspan.2005.1542652

[2] Agrawal R, Shafer J C. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, 2004, 8(6): 962-969P.

[3] Agrawal R, Imiclinski T, Swami A. Mining Assoeiation Rules between Sets of Items in Large Databases. Proeeedings of the ACM SIGMOD conference, Washington DC, ACM Press NY, 1993: 207-216P.

DOI: 10.1145/170036.170072

[4] Agrawal R, Srikant R. Fast Algorithm for Mining Association Rules. Proeeeding Intenational conference on Very Large Databases, Morgan Kaufmann Publishers, 1999: 487-499P.

[5] Feng Tao, Fionn Murtagh, Mohsen Farid. Weighted Association Rule Mining using Weighted Support and Significance Framework. In Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, 2003: 145-148P.

DOI: 10.1145/956750.956836

[6] Raymond Kosala, Hendrik Blockeel. Web Mining Research: A Survey. In Proc. ACM SIGKDD, 2000(2): 1-15P.

[7] M. S Chen, J. S Park, P. S Yu. Data mining for path traversal patterns in a web environment. In Proceedings of the 16th International Conference On Distributed Computer System, 2007: 385-392P.

DOI: 10.1109/icdcs.1996.507986

[8] R. Agrawal, T. lmielinski, A. Swami. Mining Association Rules Between Sets of Items in Large Databases. Proc. 1993 ACM SIGMOD Int'1 Conf. Management of Data, 1993: 207-216P.

DOI: 10.1145/170036.170072

[9] Park J S, Chen M S, Yu P S. Efficient parallel data mining for association rules. In Proceedings of the 4th International Conference on Information and Knowledge Management, 2002(11): 86-90P.

[10] Agrawal R, Imielinski T, Swami A. Database Mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering, 1993, 5(6): 914-925P.

DOI: 10.1109/69.250074

[11] Hannu Toivonen, Mika Klemettinen, Pirjo Ronkaine et al. Pruning and grouping discovered association rules. In M L net Workshop on Statistics, Machine Learning and Discovery in databases. Heraklion, Crete, Greece, 2007(9): 58-60P.

[12] HongJun Lu, Rudy Setiono, Huan Liu. Neurorule: a connectionist approach to data mining. In Proc. of the 21st VLDB conf., Zurich, Swizerland, 2005: 56-58P.