Impact Factor:  8.263

DOI number:  10.1016/j.asoc.2017.09.013

Journal:  Applied Soft Computing Journal

Key Words:  Frequent itemsets tree; Interpretability; Temporal association rule; Temporal relationship

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.

Indexed by:  Journal paper

Discipline:  Engineering

Document Type:  J

Volume:  62

Page Number:  817 - 829

ISSN No.:  15684946

Translation or Not:  no

Date of Publication:  2018-01-01

Included Journals:  SCI

Links to published journals:  https://www.sciencedirect.com/science/article/pii/S1568494617305525