Article ID Journal Published Year Pages File Type
6904319 Applied Soft Computing 2018 29 Pages PDF
Abstract
A novel framework for mining temporal association rules by discovering itemsets with frequent itemsets tree is introduced. In order to solve the problem of handling time series by including temporal relation between the multi items into association rules, a frequent itemsets tree is constructed in parallel with mining frequent itemsets to improve the efficiency and interpretability of rule mining without generating candidate itemsets. Experimental results show that our algorithm can provide better efficiency and interpretability in mining temporal association rules in comparison with other algorithms and has good application prospects.
Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
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