Name
Abstract | ||
|---|---|---|
Data mining research has concentrated on inventing novel methods for finding interesting information from large masses of
data. This has indeed led to many new computational tasks and some interesting algorithmic developments. However, there has
been less emphasis on issues of significance testing of the discovered patterns or models. We discuss the issues in testing
the results of data mining methods, and review some of the recent work in the development of scalable algorithmic techniques
for randomization tests for data mining methods. We consider suitable null models and generation algorithms for randomization
of 0-1 -matrices, arbitrary real valued matrices, and segmentations. We also discuss randomization for database queries.
|
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85713-6_1 | ADBIS (Local Proceedings) |
Keywords | Field | DocType |
randomization techniques,data mining research,data mining method,scalable algorithmic technique,generation algorithm,database query,interesting algorithmic development,large masse,data mining methods,significance testing,randomization test,interesting information,data mining,null model,generic algorithm,random testing | Data mining,Significance testing,Computer science,Matrix (mathematics),Randomization techniques,Theoretical computer science,Database,Scalability | Conference |
Volume | ISSN | Citations |
5207 | 0302-9743 | 3 |
PageRank | References | Authors |
0.48 | 4 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heikki Mannila | 1 | 6595 | 1495.69 |