988 research outputs found
Incrementally updating the high average-utility patterns with pre-large concept
High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases.
Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility
as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus
providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to
efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the
past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to
re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset
(HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction
insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply
the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if
the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could
not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct
HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the
state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns
and scalability.publishedVersio
Incrementally updating the high average-utility patterns with pre-large concept
High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases.
Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility
as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus
providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to
efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the
past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to
re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset
(HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction
insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply
the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if
the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could
not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct
HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the
state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns
and scalability.publishedVersio
Privacy Preserving Utility Mining: A Survey
In big data era, the collected data usually contains rich information and
hidden knowledge. Utility-oriented pattern mining and analytics have shown a
powerful ability to explore these ubiquitous data, which may be collected from
various fields and applications, such as market basket analysis, retail,
click-stream analysis, medical analysis, and bioinformatics. However, analysis
of these data with sensitive private information raises privacy concerns. To
achieve better trade-off between utility maximizing and privacy preserving,
Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent
years. In this paper, we provide a comprehensive overview of PPUM. We first
present the background of utility mining, privacy-preserving data mining and
PPUM, then introduce the related preliminaries and problem formulation of PPUM,
as well as some key evaluation criteria for PPUM. In particular, we present and
discuss the current state-of-the-art PPUM algorithms, as well as their
advantages and deficiencies in detail. Finally, we highlight and discuss some
technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page
Efficient chain structure for high-utility sequential pattern mining
High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates
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