Name
Abstract | ||
|---|---|---|
With increasing in amount of available data, researchers try to propose new approaches for extracting useful knowledge. Association Rule Mining (ARM) is one of the main approaches that became popular in this field. It can extract frequent rules and patterns from a database. Many approaches were proposed for mining frequent patterns; however, heuristic algorithms are one of the promising methods and many of ARM algorithms are based on these kinds of algorithms. In this paper, we improve our previous approach, ARMICA, and try to consider more parameters, like the number of database scans, the number of generated rules, and the quality of generated rules. We compare the proposed method with the Apriori, ARMICA, and FP-growth and the experimental results indicate that ARMICA-Improved is faster, produces less number of rules, generates rules with more quality, has less number of database scans, it is accurate, and finally, it is an automatic approach and does not need predefined minimum support and confidence values. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-63558-3_25 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Association rules mining,Data mining,Imperialist Competitive Algorithm (ICA) | Data mining,Heuristic,Computer science,A priori and a posteriori,Association rule learning,Artificial intelligence,K-optimal pattern discovery,Machine learning | Conference |
Volume | ISSN | Citations |
10412 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahpar Yakhchi | 1 | 0 | 0.34 |
| Seyed Mohssen Ghafari | 2 | 8 | 3.50 |
| Christos Tjortjis | 3 | 173 | 24.40 |
| Mahdi Fazeli | 4 | 271 | 31.45 |