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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.


The final merged utility-list structures for the updated database.
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fig4: The final merged utility-list structures for the updated database.

Mentions: After that, the utility-list structures from the original database and the incremental ones are merged together. For example, the utility-list structure of (B) in the original database is UL(B) = {TID, Iutility(B),  Rutility(B)} = {(1,300,159), (2,300,203), (3,450,6)}. The utility-list structure of (B) in the incremental database is UL(B)′ = {TID, Iutility(B), Rutility(B)} = {(15,450,12)}. The utility-list structures for (B) are then updated as {(1,300,159), (2,300,203), (3,450,6), (15,450,12)}. The other items {A, C, D, E, F} are processed in the same way. After that, the final updated utility-list structures are then updated and shown in Figure 4.


An incremental high-utility mining algorithm with transaction insertion.

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

The final merged utility-list structures for the updated database.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: The final merged utility-list structures for the updated database.
Mentions: After that, the utility-list structures from the original database and the incremental ones are merged together. For example, the utility-list structure of (B) in the original database is UL(B) = {TID, Iutility(B),  Rutility(B)} = {(1,300,159), (2,300,203), (3,450,6)}. The utility-list structure of (B) in the incremental database is UL(B)′ = {TID, Iutility(B), Rutility(B)} = {(15,450,12)}. The utility-list structures for (B) are then updated as {(1,300,159), (2,300,203), (3,450,6), (15,450,12)}. The other items {A, C, D, E, F} are processed in the same way. After that, the final updated utility-list structures are then updated and shown in Figure 4.

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.