Mining Weighted Erasable Itemsets Over the Incremental Database Based on the InvP-List
International Journal of Machine Learning and Computing · 2022
Abstract-An erasable itemset is the low profit itemset in the product database.The previous algorithms for mining erasable itemsets ignore the weight of each component of the product and mine erasable itemsets by concerning the product profit only in static product databases.But, when we consider the weight of each component, previous algorithms for mining weighted erasable itemsets would violate the anti-monotone property.That is, the subset X of an erasable pattern Y may not be an erasable pattern.The IWEI algorithm uses the static overestimated factor of itemsets profits to satisfy the "antimonotone property" of weighted erasable itemset and constructs the IWEI-Tree and OP-List data structure for the dynamic database.However, the IWEI-Tree has to be reconstructed, when reading the whole product database is finished.It will take long time to complete the mining of the whole tree, if the database is frequently updated.The IWEI algorithm generates the too low static value of the overestimated factor to prune candidates.To solve those problems, in this paper, we propose the Inverted-Product-List algorithm (InvP-List) and with the local estimated factor to identify weighted erasable itemsets candidates from the Candidate-List which is generated from InvP-List.We propose the appropriate estimated factor to reduce the number of candidates which is called LMAW.LMAW is a local estimated factor which is used to check whether the itemset is a weighted erasable itemset or not.Our InvP-List algorithm also requires only one database scan.Moreover, our proposed algorithm concerning the local estimated factor creates few numbers of candidates than the IWEI algorithm.From the performance study, we show that our InvP-List algorithm is more efficient than the IWEI algorithm both in the real and the synthetic datasets.