Name
Abstract | ||
|---|---|---|
Outlier detection is concerned with discovering exceptional behaviors of objects in data sets.It is becoming a growingly useful tool in applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, identifying computer intrusion, detecting health problems, etc. In this paper, we introduce a connectivity-based outlier factor (COF) scheme that improves the effectiveness of an existing local outlier factor (LOF) scheme when a pattern itself has similar neighbourhood density as an outlier. We give theoretical and empirical analysis to demonstrate the improvement in effectiveness and the capability of the COF scheme in comparison with the LOF scheme. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-47887-6_53 | PAKDD |
Keywords | Field | DocType |
outlier detection,outlier detections,empirical analysis,existing local outlier factor,cof scheme,lof scheme,low density patterns,credit card fraud detection,computer intrusion,criminal behavior,connectivity-based outlier factor,enhancing effectiveness,e commerce,intrusion detection | Local outlier factor,Data mining,Anomaly detection,Credit card fraud,Intrusion,Computer science,Outlier,Artificial intelligence,Connectivity,Time complexity,Machine learning,Low density | Conference |
ISBN | Citations | PageRank |
3-540-43704-5 | 123 | 4.44 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Zhixiang Chen | 2 | 396 | 33.28 |
| Ada Wai-Chee Fu | 3 | 4646 | 417.59 |
| David Wai-Lok Cheung | 4 | 2469 | 282.09 |