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 constructed utility-list structures of 1-itemsets.
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Related In: Results  -  Collection


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fig1: The constructed utility-list structures of 1-itemsets.

Mentions: In the construction process, the itemsets are sorted in ascending order of their transaction-weighted utility (TWU). For the Rutility of an itemset X in a transaction, it keeps the rest utilities in the transaction except the processed itemset X. Since the TWU values of the itemsets are changed with transaction insertion, the sorted order of the utility-list structures and the Rutility value should also be changed. The number of inserted transactions is, however, very small compared to the original database. In the proposed algorithm, the sorted order of the itemsets in the inserted transactions follows the initially TWU ascending order of itemsets in the original database. An example to show the utility-list structures of 1-itemsets is shown in Figure 1.


An incremental high-utility mining algorithm with transaction insertion.

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

The constructed utility-list structures of 1-itemsets.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: The constructed utility-list structures of 1-itemsets.
Mentions: In the construction process, the itemsets are sorted in ascending order of their transaction-weighted utility (TWU). For the Rutility of an itemset X in a transaction, it keeps the rest utilities in the transaction except the processed itemset X. Since the TWU values of the itemsets are changed with transaction insertion, the sorted order of the utility-list structures and the Rutility value should also be changed. The number of inserted transactions is, however, very small compared to the original database. In the proposed algorithm, the sorted order of the itemsets in the inserted transactions follows the initially TWU ascending order of itemsets in the original database. An example to show the utility-list structures of 1-itemsets is shown in Figure 1.

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.