An Approach to the Mining of User Focused Frequent Itemsets Based on Attention

Abstract:

Usually, non trivial network visiting behaviors implied in network visiting log can be treated as the frequent itemsets or association rules if data in networking log file are transformed into transaction and technologies on association rule can be used to mine those frequent itemsets which are focused by user or some application. To mine non trivial behaviors of network visiting effectively, an attention based frequent itemsets mining method is proposed in this paper. In our proposed method, properties of users focusing is described as attention set and the early selection model of attention as information filter is referenced in the design of our method. Experimental results show that our proposed method is faster than apriori algorithm on the mining of frequent itemsets which is focused by our attention.

• Periodical: Applied Mechanics and Materials (Volumes 536-537)
• Pages: 520-523
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.536-537.520
• Online since: April 2014
• Authors: Jia Liu, Zhen Ya Zhang*, Hong Mei Cheng, Qian Sheng Fang
• Keywords: Association Rule, Attention, Frequent Itemsets, User

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