A new algorithm for frequency tendency prediction over data streams
GUO Li-chao1, SU Hong-ye1, GOU Qian-wen2
1. Institute of Cyber-System and Control, Zhejiang University, Hangzhou 310027, China;
2. College of Public Administration, Zhejiang University, Hangzhou 310027, China
For the frequency tendency prediction problem of itemsets over streams, a novel max-min-frequency tendency prediction (MMFTP) algorithm is proposed based on the Max-Min Frequency Window model. A max-min-frequency pattern Tree (MMFP-Tree) structure is established to store the summary information of streams a new measure frequency changing rate (FCR) is presented to describe the frequency tendency of itemsets quantitatively. The MM-FTP algorithm is useful in the index tendency prediction and the confidence prediction of classification. Based on the result of the case study on web log data stream, the MM-FTP algorithm could be used to predict the frequency tendency efficiently and effectively.
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