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.


The utility-list structures for the supersets of C.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4355605&req=5

fig5: The utility-list structures for the supersets of C.

Mentions: In this example, since the utility-list structures are sorted in ascending order of their TWU values, the item (C) is first processed to mine the related HUIs of (C). The total utility of (C) in the utility-list structure can be directly derived from Iutility, which can be calculated as (5 + 3 + 2 + 3 + 4 + 3 + 2)  ( = 22). The Rutility of (C) is calculated as (69 + 143 + 576 + 150 + 409 + 359 + 309)  ( = 2015). Since the summation of Iutility(C) is smaller than the updated minimum utility count, the summation of Iutility(C) and Rutility(C) is larger than minimum utility count as (22 + 2015 > 1607.2). Thus, the depth-search mechanism is then performed to find the supersets of the item (C) in the enumeration tree. The item (C) is then combined with item (F). Both of them are appeared in transactions 3, 4, and 7, which can be observed from Figure 3, to construct the utility-list structures for (CF). The other items (E, B, D, A) are processed in the same way. After that, the supersets of (C) are shown in Figure 5.


An incremental high-utility mining algorithm with transaction insertion.

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

The utility-list structures for the supersets of C.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: The utility-list structures for the supersets of C.
Mentions: In this example, since the utility-list structures are sorted in ascending order of their TWU values, the item (C) is first processed to mine the related HUIs of (C). The total utility of (C) in the utility-list structure can be directly derived from Iutility, which can be calculated as (5 + 3 + 2 + 3 + 4 + 3 + 2)  ( = 22). The Rutility of (C) is calculated as (69 + 143 + 576 + 150 + 409 + 359 + 309)  ( = 2015). Since the summation of Iutility(C) is smaller than the updated minimum utility count, the summation of Iutility(C) and Rutility(C) is larger than minimum utility count as (22 + 2015 > 1607.2). Thus, the depth-search mechanism is then performed to find the supersets of the item (C) in the enumeration tree. The item (C) is then combined with item (F). Both of them are appeared in transactions 3, 4, and 7, which can be observed from Figure 3, to construct the utility-list structures for (CF). The other items (E, B, D, A) are processed in the same way. After that, the supersets of (C) are shown in Figure 5.

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.