Name
Abstract | ||
|---|---|---|
Association rule algorithms can produce a very large number of output patterns. This has raised questions of whether the set of discovered rules "overfit" the data because all the patterns that satisfy some constraints are generated (the Bonferroni effect). In other words, the question is whether some of the rules are "false discoveries" that are not statistically significant. We present a novel approach for estimating the number of "false discoveries" at any cutoff level. Empirical evaluation... |
Year | Venue | Keywords |
|---|---|---|
1998 | KDD | statistical significance,satisfiability,association rule |
Field | DocType | Citations |
Data mining,Bonferroni correction,Computer science,Cutoff,Association rule learning,Large numbers,Artificial intelligence,Overfitting,Surprise,Confidence interval,Machine learning | Conference | 55 |
PageRank | References | Authors |
4.83 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nimrod Megiddo | 1 | 4244 | 668.46 |
| Ramakrishnan Srikant | 2 | 13349 | 1804.96 |