ABSTRACT
In this paper, a new pattern-growth algorithm is presented to mine frequent sequential patterns using First-Occurrence Forests (FOF). This algorithm uses a simple list of pointers to the first-occurrences of a symbol in the aggregate tree [1], as the basic data structure for database representation, and does not rebuild aggregate trees for projection databases. The experimental evaluation shows that our new FOF mining algorithm outperforms the PLWAP-tree mining algorithm [2] and the FLWAP-tree mining algorithm [3], both in the mining time and the amount of memory used.
- Myra Spiliopoulou and Lukas C. Faulstich. WUM: A tool for web utilization analysis. In Proceedings of EDBT Workshop Web DB'98. Springer Verlag, LNCS 1590, 1998. Google ScholarDigital Library
- Christie I. Ezeife and Yi Lu. Mining web log sequential patterns with position coded pre-order linked wap-tree. International Journal of Data Mining and Knowledge Discovery, 10:5--38, 2005. Google ScholarDigital Library
- Peiyi Tang, Markus P. Turkia, and Kyle A. Gallivan. Mining web access patterns with first-occurrence linked WAP-trees. In Proceedings of the 16th International Conference on Software Engineering and Data Engineering (SEDE'07), pages 247--252, Las Vegas, USA, July 2007.Google Scholar
- Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the International Conference on Extending Database Technology, pages 3--17, 1996. Google ScholarDigital Library
- Jian Pei, Jiawei Han, Behzad Mortazavi-asl, and Hua Zhu. Mining access patterns efficiently from web logs. In Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'00), pages 396--407. Lecture Notes in Computer Science, Vol. 1805, 2000. Google ScholarDigital Library
- Jiawei Han, Jian Pei, and Yiwen Yin. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International on Management of Data, pages 1--12. ACM Press, 2000. Google ScholarDigital Library
Index Terms
- Mining frequent sequential patterns with first-occurrence forests
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