Name
Abstract | ||
|---|---|---|
Association-rule mining has heretofore relied on the condition of high support to do its work efficiently. In particular, the well-known a priori algorithm is only effective when the only rules of interest are relationships that occur very frequently. However, there are a number of applications, such as data mining, identification of similar web documents, clustering, and collaborative filtering, where the rules of interest have comparatively few instances in the data. In these cases, we must look for highly correlated items, or possibly even causal relationships between infrequent items. We develop a family of algorithms for solving this problem, employing a combination of random sampling and hashing techniques. We provide analysis of the algorithms developed and conduct experiments on real and synthetic data to obtain a comparative performance analysis. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/69.908981 | Knowledge and Data Engineering, IEEE Transactions |
Keywords | Field | DocType |
data mining,database theory,software performance evaluation,very large databases,Web documents,association rule mining,causal relationships,collaborative filtering,data mining,experiments,hashing,large databases,performance analysis,random sampling,similarity metric | Data mining,Importance sampling,Data stream mining,Collaborative filtering,Computer science,Association rule learning,Synthetic data,Sampling (statistics),Hash function,Cluster analysis,Database | Conference |
Volume | Issue | ISSN |
13 | 1 | 1041-4347 |
ISBN | Citations | PageRank |
0-7695-0506-6 | 249 | 26.60 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edith Cohen | 1 | 3260 | 268.21 |
| Datar, M. | 2 | 249 | 26.60 |
| Fujiwara, S. | 3 | 249 | 26.60 |
| Aristides Gionis | 4 | 6808 | 386.81 |