An efficient pattern growth approach for mining fault tolerant frequent itemsets

Abstract

Mining fault tolerant (FT) frequent itemsets from transactional databases are computationally more expensive than mining exact matching frequent itemsets. Previous algorithms mine FT frequent itemsets using Apriori heuristic. Apriori-like algorithms generate exponential number of candidate itemsets including the itemsets that do not exist in the database. These algorithms require multiple scans of database for counting the support of candidate FT itemsets. In this paper we present a novel algorithm, which mines FT frequent itemsets using frequent pattern growth approach (FT-PatternGrowth). FT-PatternGrowth adopts a divide-and-conquer technique and recursively projects transactional database into a set of smaller projected transactional databases and mines FT frequent itemsets in each projected database by exploring only locally frequent items. This mines the complete set of FT frequent itemsets and substantially reduces those candidate itemsets that do not exist in the database. FT-PatternGrowth stores the transactional database in a highly condensed much smaller data structure called frequent pattern tree (FP-tree). The support of candidate itemsets are counted directly from the FP-tree without scanning the original database multiple times. This improves the processing speed of algorithm. Our experiments on benchmark databases indicates mining FT frequent itemsets using FT-PatternGrowth is highly efficient than Apriori-like algorithms.