Data Mining Algorithm of Frequent Probability Item Based on Sliding Window

Zhi Zhang; Qi Fu

doi:10.4028/www.scientific.net/AMM.602-605.3268

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Data Mining Algorithm of Frequent Probability Item...

Data Mining Algorithm of Frequent Probability Item Based on Sliding Window

Abstract:

In order to meet the uncertain data stream mining demand in large dynamic database, a frequent probability item mining algorithm was proposed base on sliding window. The mass data in the database was regarded as a data stream. In the window model of data stream, the frequent item set was extracted according to the probability frequency distribution information of data. Compared to the traditional algorithm, the mining environmental constraints of the certain data stream was overcome, the defect that the relevant information was easy to lose was improved. The true information of data was reflected fully, and the most accurate frequent item was minded. Simulation result shows that the new algorithm can mine the frequent items accurately, and the accuracy rate is higher than the traditional method. It can process the data quickly. It provides effective strategy for analyzing the large database, and it can meet the memory requirement and performance requirement in database analysis and mining.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

3268-3271

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.3268

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Online since:

August 2014

Authors:

Zhi Zhang, Qi Fu*

Keywords:

Data Stream, Frequent Item Set, Probability Frequency Distribution, Uncertain Data

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* - Corresponding Author

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