Name
Abstract | ||
|---|---|---|
In the process of OLAP analysis based on multi-dimensional data, analysts are often involved in large-scale data cube, which results users cannot find the interest information efficiently. To overcome this problem, some exceptions mining or exceptions-based methods were proposed. In this paper, a new regression-based definition of exception is proposed, threshold exception, and following which an exception mining algorithm is proposed to help users find the exceptions in the data cells effectively using regression parameters. This method estimates the data as exception by comparing its normalized residual to the thresholds user gave. Performance study shows that the method is practical and effective. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.372 | FSKD (5) |
Keywords | Field | DocType |
new regression-based definition,large-scale data cube,exception mining algorithm,regression based definition,olap analysis,multi-dimensional data cube,interest information,data cell,threshold exception,exception handling,exceptions mining,data mining,very large databases,multidimensional data cube,exceptions-based method,efficient exception mining algorithm,multi-dimensional data,data engineering,feature extraction,algorithm design and analysis,data cube,knowledge engineering,regression analysis | Data mining,Residual,Algorithm design,Regression analysis,Computer science,Exception handling,Feature extraction,Information engineering,Artificial intelligence,Online analytical processing,Data cube,Machine learning | Conference |
Volume | ISBN | Citations |
5 | 978-0-7695-3735-1 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youwei Ding | 1 | 2 | 1.05 |
| Kongfa Hu | 2 | 38 | 9.26 |
| Ling Chen | 3 | 217 | 29.30 |