Affiliations: [a] Innovative Information Industry Research Center, Shenzhen, Guangdong, China | [b] School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Shenzhen, Guangdong, China | [c] Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan | [d] Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
Correspondence:
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Corresponding author: Jerry Chun-Wei Lin, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China. E-mail:jerrylin@ieee.org
Abstract: High-utility itemset mining (HUIM) has been recently
studied to mine high-utility itemsets (HUIs) from the transactional database
by considering more factors such as profit and quantity. Many approaches
have been proposed for HUIM from a static database. Fewer studies have been
developed to maintain the discovered HUIs in dynamic environment whether
transaction insertion or transaction deletion. In the past, the FUP-HUI-DEL
and PRE-HUI-DEL algorithms were respectively proposed to effectively
maintain the discovered high transaction-weighted utilization itemsets
(HTWUIs) and high-utility itemsets (HUIs) when the transactions are
consequentially deleted from the original database. The original database is
still, however, required to be rescanned when small transaction-weighted
utilization itemsets in the original database are necessary to be
maintained. In this paper, an efficient algorithm namely HUI-list-DEL is
presented to discover HUIs by maintaining the built utility-list structure
for transaction deletion in dynamic databases. Based on the designed
algorithm, the HUIs can be directly produced without candidate generation or
the numerous database scans. Two pruning strategies are also designed to
speed up the maintenance approach of HUIs. Substantial experiments show that
the proposed maintenance approach for transaction deletion significantly
outperforms the previous approaches in terms of execution time, memory
consumption and scalability.
