Limits...
An incremental high-utility mining algorithm with transaction insertion.

Lin JC, Gan W, Hong TP, Zhang B - ScientificWorldJournal (2015)

Bottom Line: High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures.Most algorithms of high-utility mining are designed to handle the static database.Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, Shenzhen University Town, Xili, Shenzhen 518055, China.

ABSTRACT
Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.

No MeSH data available.


Memory consumption under various insertion ratios.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig9: Memory consumption under various insertion ratios.

Mentions: From Figure 8, it can be observed that the FHM and the proposed algorithms require steady memory along with the increasing of MUs compared to the other algorithms. This is because the fact that the FHM and the proposed algorithms are necessary to build the utility-list structures for keeping the itemsets. When MU is set lower, the proposed algorithm requires fewer memory than the other algorithms, which can be observed from Figure 8(a). Experiments are then conducted to show the comparisons under various IRs with a fixed MU. The results are shown in Figure 9.


An incremental high-utility mining algorithm with transaction insertion.

Lin JC, Gan W, Hong TP, Zhang B - ScientificWorldJournal (2015)

Memory consumption under various insertion ratios.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4355605&req=5

fig9: Memory consumption under various insertion ratios.
Mentions: From Figure 8, it can be observed that the FHM and the proposed algorithms require steady memory along with the increasing of MUs compared to the other algorithms. This is because the fact that the FHM and the proposed algorithms are necessary to build the utility-list structures for keeping the itemsets. When MU is set lower, the proposed algorithm requires fewer memory than the other algorithms, which can be observed from Figure 8(a). Experiments are then conducted to show the comparisons under various IRs with a fixed MU. The results are shown in Figure 9.

Bottom Line: High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures.Most algorithms of high-utility mining are designed to handle the static database.Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, Shenzhen University Town, Xili, Shenzhen 518055, China.

ABSTRACT
Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.

No MeSH data available.