An efficient pattern growth approach for mining fault tolerant frequent itemsets
作者：
Highlights：
• Mining fault tolerant (FT) frequent itemsets are computationally expensive.
• Related algorithms are Apriori-like candidate generation-and-test approaches.
• Apriori-like algorithms generate exponential number of candidate itemsets.
• We propose mining FT frequent itemsets using frequent pattern growth approach.
• The proposed approach mines complete set of itemsets with less computational cost.
摘要
•Mining fault tolerant (FT) frequent itemsets are computationally expensive.•Related algorithms are Apriori-like candidate generation-and-test approaches.•Apriori-like algorithms generate exponential number of candidate itemsets.•We propose mining FT frequent itemsets using frequent pattern growth approach.•The proposed approach mines complete set of itemsets with less computational cost.
论文关键词：Fault tolerant frequent itemset mining,Frequent itemset mining,Pattern growth,Association rules mining
论文评审过程：Received 13 March 2019, Revised 27 September 2019, Accepted 20 October 2019, Available online 21 October 2019, Version of Record 31 October 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113046
