Name
Abstract | ||
|---|---|---|
Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46675-0_47 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Neighborhood,Rough set,Anomaly detection,Outlier detection | Data source,Data mining,Anomaly detection,Data set,Pattern recognition,Categorical variable,Computer science,Correctness,Outlier,Rough set,Artificial intelligence | Conference |
Volume | ISSN | Citations |
9949 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pey Yun Goh | 1 | 1 | 1.36 |
| Shing Chiang Tan | 2 | 122 | 18.99 |
| Wooi Ping Cheah | 3 | 36 | 8.03 |