Mining Frequent Items in Uncertain Dataset

Abstract:

Because of uncertainty data, traditional algorithm of mining frequent items in certain dataset is difficult to apply to uncertain dataset. Considering characteristics of uncertain data, an improved vertical mining algorithm to find frequent items in uncertain dataset was proposed with the algorithm thought of classic vertical algorithm-Eclat in certain dataset. The improved algorithm merged TID field and corresponding probability field into probability vector. During the expansion of itemset and probability vector, itemset tree was established, and the support of candidate itemsets was calculated by means of vector operations. The improved algorithm is proved to be feasible and efficient according to experimental comparison and analysis.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 2862-2865
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.2862
• Online since: August 2013
• Authors: Chao Quan Chen, Jia Huan Huang, Yun Hui Jiang
• Keywords: Frequent Items, Probability Field, Uncertain Dataset, Vector, Vertical Mining

References

[1] Chui C K, Kao B, Hung E. Mining frequent itemsets from uncertain data . Proc of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2007: 47-58.

DOI: 10.1007/978-3-540-71701-0_8

[2] Aggarwal C C, Li Yan, Wang Jian-yong. Frequent pattern mining with uncertain data . Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York: ACM Press, 2009: 29-38.

DOI: 10.1145/1557019.1557030

[3] Chun-Wei Lin, Tzung-Pei Hong. A new mining approach for uncertain databases using CUFP trees . Expert Systems with Application 39(2012) 4084-4093.

DOI: 10.1016/j.eswa.2011.09.087

[4] Liyi Zhang, Shouzhi Zhang, Bole Shi. Vertical data mining algorithm for frequent patterns in uncertain data set. Journal of Chinese Computer Systems, 2012, 33(2): 206-209.

[5] Wang Jinmiao. Methods of mining frequent items in uncertain data set. Computer Engineering and Applications, 2011, 47(20): 121-125.