Name
Abstract | ||
|---|---|---|
In this paper, an alternative approach for maximal association rules mining from a transactional database using soft set theory is proposed. The first step of the proposed approach is based on representing a transactional database as a soft set. Based on the soft set, the notion of items co-occurrence in a transaction can be defined. The definitions of soft maximal association rules, maximal support and maximal confidence are presented using the concept of items co-occurrence. It is shown that by using soft set theory, maximal rules discovered are identical and faster as compared to traditional maximal and rough maximal association rules approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10583-8_19 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Data mining,Maximal association rules,Soft set theory | Data mining,Computer science,Soft set,Theoretical computer science,Association rule learning,Database transaction | Conference |
Volume | ISSN | Citations |
64 | 1865-0929 | 9 |
PageRank | References | Authors |
0.48 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tutut Herawan | 1 | 608 | 75.21 |
| Iwan Tri Riyadi Yanto | 2 | 64 | 7.29 |
| Mustafa Mat Deris | 3 | 510 | 56.25 |