Name
Abstract | ||
|---|---|---|
In this paper, we describe a set-based approach for mining association rules and finding frequent sequential patterns in customer transactional databases. The set-based approach is a direct improvement of the original association rule mining algorithms proposed by R. Agrawal and R. Skrikant. Our approach relaxes the constraints described in Apriori(All/Some), and improves the performance while being more user-oriented and self-adaptive than the probabilistic knowledge representation. We compare the performance of the improved algorithms with results from an experimental study. The approach can be extended to more set-based mathematical models for further data analysis in order to discover hidden knowledge and patterns with the improved workflow and set-based representation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCIS.2009.5291851 | 2009 24TH INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES |
Keywords | Field | DocType |
data mining, sequential patterns, assocation rules | Data mining,Set theory,Time series,Knowledge representation and reasoning,Algorithm design,Computer science,A priori and a posteriori,Association rule learning,Artificial intelligence,Probabilistic logic,Workflow,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shang Gao | 1 | 291 | 59.33 |
| Reda Alhajj | 2 | 1919 | 205.67 |
| Jon G. Rokne | 3 | 263 | 45.63 |
| Jiwen Guan | 4 | 88 | 6.19 |