A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream


Article Preview

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model. A false negative method is proposed based on Chernoff Bound to save the computing and memory cost. Our experimental results on a real world dataset show that our algorithm is effective and efficient.



Edited by:

Robin G. Qiu and Yongfeng Ju






H. F. Li and N. Zhang, "A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream", Applied Mechanics and Materials, Vols. 135-136, pp. 21-25, 2012

Online since:

October 2011




[1] R. Agrawal, and R. Srikant, Fast algorithms for mining association rules, in: Proc. VLDB'(1994).

[2] J. Han, H. Cheng, D. Xin, and X. Yan, Frequent pattern mining: current status and future directions, Data Mining and Knowledge Discovery, 17 (2007) 55-86.

DOI: 10.1007/s10618-006-0059-1

[3] J. Han, J. Pei, Y. Yin, and R. Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, in: DMKD'(2004).

DOI: 10.1023/b:dami.0000005258.31418.83

[4] S. Kevin, and R. Ramakrishnan, Bottom-Up Computation of Sparse and Iceberg CUBEs, in: Proc. SIGMOD'(1999).

[5] D. Lee, and W. Lee, Finding Maximal Frequent Itemsets over Online Data Streams Adaptively, in: Proc. ICDM'(2005).

DOI: 10.1109/icdm.2005.68

[6] G. Mao, X. Wu, X. Zhu, and G. Chen, Mining Maximal Frequent Itemsets from Data Streams, Journal of Information Science 33 (3) (2007) 251-262.

DOI: 10.1177/0165551506068179

[7] H.J. Woo, and W.S. Lee, estMax: Tracing Maximal Frequent Itemsets over Online Data Streams, in: Proc. ICDM'(2007).

DOI: 10.1109/icdm.2007.70

[8] H. Li, S. Lee, and M. Shan. Online Mining(Recently) Maximal Frequent Itemsets over Data Streams, in: Proc. RIDE'(2005).

DOI: 10.1109/ride.2005.13

In order to see related information, you need to Login.