Name
Abstract | ||
|---|---|---|
Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. We develop an efficient approach to log-linear analysis that scales to hundreds of variables by melding the classical statistical machinery of log-linear analysis with advanced data mining techniques from association discovery and graphical modeling. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDM.2013.17 | Data Mining |
Keywords | Field | DocType |
data mining,statistical analysis,advanced data mining techniques,association discovery,classical statistical machinery,data mining task,graphical modeling,high-dimensional data,log-linear analysis scaling,primary statistical approach,Association Discovery,Data Modeling,High-dimensional Data,Log-linear Analysis | Data modeling,Data mining,Clustering high-dimensional data,Log-linear analysis,Computer science,Maximum likelihood,Particle separators,Exploratory data analysis,Scaling,Statistical analysis | Conference |
ISSN | Citations | PageRank |
1550-4786 | 7 | 0.51 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| François Petitjean | 1 | 474 | 34.26 |
| Geoffrey I. Webb | 2 | 3130 | 234.10 |
| Ann E. Nicholson | 3 | 692 | 88.01 |