Incremental update on probabilistic frequent itemsets in uncertain databases
Ming-Yen Lin, Cheng-Tai Fu, Sue-Chen Hsueh · 2012
Mining frequent itemsets in an uncertain database is a highly complicated problem. Most algorithms focus on improving the mining efficiency with the assumption that the database is static. Uncertain databases, however, are constantly updated with newly appended transactions like certain databases. Some patterns may become obsolete and new ones may emerge due to updates. Remining the whole uncertain database from scratch is very time-consuming owing to the frequentness probabilities computations. To tackle this maintenance problem, we propose an algorithm called p-FUP for efficient incremental update of patterns in an uncertain database. The p-FUP algorithm, inspired by a threshold-based PFI-testing technique and the FUP algorithm, uses approximations to incrementally update and discovers frequent itemsets in the uncertain database. Comprehensive experiments using both real and synthetic datasets show that p-FUP outperforms the re-mining based algorithm of 2.8 times faster in average, and has good linear scalability.