http://repository.vnu.edu.vn/handle/VNU_123/27330
The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/fre-quent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs.
Lattice-based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice.
Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs.
Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again.
In this article, we describe a number of existing LBAs for mining ARs on static data-bases including lattice building and rule generation.
In addition, in today’s online system, the data often change in several operations such as insert, delete, and update.
Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed
The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/fre-quent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs.
Lattice-based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice.
Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs.
Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again.
In this article, we describe a number of existing LBAs for mining ARs on static data-bases including lattice building and rule generation.
In addition, in today’s online system, the data often change in several operations such as insert, delete, and update.
Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed
| Title: | The lattice-based approaches for mining association rules: a review |
| Authors: | Le, Tuong Vo, Bay |
| Keywords: | Mining association rules Traditional methods Data mining Algorithm |
| Issue Date: | 2016 |
| Publisher: | H. : ĐHQGHN |
| Citation: | ISIKNOWLEDGE |
| Abstract: | The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/fre-quent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice-based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static data-bases including lattice building and rule generation. In addition, in today’s online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed |
| Description: | WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY Volume: 6 Issue: 4 Pages: 140-151 ; TNS06419 |
| URI: | http://repository.vnu.edu.vn/handle/VNU_123/27330 |
| Appears in Collections: | Bài báo của ĐHQGHN trong Web of Science |
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