Name
Abstract | ||
|---|---|---|
Analysing datasets requires sophisticated techniques which can help to unearth interesting patterns. One approach is to mine multidimensional association rules from data. The traditional association rule mining relies on uniform support and confidence values which does not always yield interesting rules due to varied nature of data. This paper presents a novel approach to mine multidimensional association rules from dataset with varying support. The improved algorithm is being proposed to overcome missing aspect of tradition rule mining algorithms like Apriori. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/COMPSAC.2009.109 | Computer Software and Applications Conference, 2009. COMPSAC '09. 33rd Annual IEEE International |
Keywords | Field | DocType |
data mining,marketing data processing,Apriori,data mining,market basket analysis,multidimensional association rules mining,variable support,Association Rule Mining,Variable support | Data mining,Algorithm design,Computer science,A priori and a posteriori,FSA-Red Algorithm,Association rule learning,Computer Applications,Affinity analysis,Application software,Multidimensional systems | Conference |
Volume | ISSN | ISBN |
2 | 0730-3157 | 978-0-7695-3726-9 |
Citations | PageRank | References |
2 | 0.37 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajul Anand | 1 | 2 | 0.37 |
| Ravi Agrawal | 2 | 2 | 0.37 |
| Joydip Dhar | 3 | 37 | 12.11 |