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 (InvPList) 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.
Index Terms—Erasable itemset, frequent patterns, itemset
pruning, local estimated factor, weight constraint.
The authors are with National Sun Yat-sen University, Taiwan (e-mail:
changyi@mail.cse.nsysu.edu.tw, jacky83528@gmail.com, rsps1008@gmail.nsysu.edu.tw).
Cite: Ye-In Chang, Siang-Jia Du, and Chin-Ting Lin, "Mining Weighted Erasable Itemsets Over the Incremental Database Based on the InvP-List," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 236-244, 2022.
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