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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ordonez, Carlos |
| Abstract | Association rules and clustering are fundamental data mining techniques used for different goals. We propose a unifying theory by proving association support and rule confidence can be bounded and estimated from clusters on binary dimensions. Three support metrics are introduced: lower, upper and average support. Three confidence metrics are proposed: lower, upper and average confidence. Clusters represent a simple model that allows understanding and approximating association rules, instead of searching for them in a large transaction data set. |
| Starting Page | 545 |
| Ending Page | 546 |
| Page Count | 2 |
| File Format | |
| ISBN | 1581139640 |
| DOI | 10.1145/1066677.1066800 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2005-03-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Support Bound Model Association rules Clustering |
| Content Type | Text |
| Resource Type | Article |
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