Mining fault tolerant frequent patterns using pattern growth approach

Mining fault tolerant (FT) frequent patterns from transactional datasets are very complex than mining all frequent patterns (itemsets), in terms of both search space exploration and support counting of candidate FT-patterns. Previous studies on mining FT frequent patterns adopt Apriori-like candidate set generation- and-test approach, in which a number of dataset scans are needed to declare a candidate FT-pattern frequent. First for checking its FT-pattern support, and then for checking its individual items support present in its FT- pattern which depends on the cardinality of pattern. Inspired from the pattern growth technique for mining frequent itemsets, in this paper we present a novel algorithm for mining FT frequent patterns using pattern growth approach. Our algorithm stores the original transactional dataset in a highly condensed, much smaller data structure called FT-FP-tree, and the FT-pattern support and item support of all the FT- patterns are counting directly from the FT-FP-tree, without scanning the original dataset multiple times. While costly candidate set generations are avoided by generating conditional patterns from FT-FP-tree. Our extensive experiments on benchmark datasets suggest that, mining FT frequent patterns using our algorithm is highly efficient as compared to Apriori-like approach.