Name
Abstract | ||
|---|---|---|
The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it is often difficult to determine which algorithms might detect anomalies given any random dataset. In this paper we provide a partial solution to this problem by elevating the search for anomalous data in transaction-oriented datasets to an inspection of the rules that can be produced by higher order longitudinal/spatio-temporal association rule mining. In this way we are able to apply algorithms that may provide a view of anomalies that is arguably closer to that sought by information analysts. |
Year | Venue | Keywords |
|---|---|---|
2007 | AIDM | data mining,anomalous longitudinal association,information analyst,spatio-temporal association rule mining,partial solution,higher order longitudinal,higher order mining,automated data analysis,anomalous data,important function,anomaly detection algorithm,random dataset,data analysis,higher order,anomaly detection |
Field | DocType | Citations |
Anomaly detection,Data mining,Computer science,Association rule learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Ping | 1 | 0 | 0.34 |
| John F. Roddick | 2 | 1908 | 331.20 |