Name
Title | ||
|---|---|---|
Capabilities of outlier detection schemes in large datasets, framework and methodologies |
Abstract | ||
|---|---|---|
Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some important applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, discovering computer intrusion, etc. In this paper, we first present a unified model for several existing outlier detection schemes, and propose a compatibility theory, which establishes a framework for describing the capabilities for various outlier formulation schemes in terms of matching users'intuitions. Under this framework, we show that the density-based scheme is more powerful than the distance-based scheme when a dataset contains patterns with diverse characteristics. The density-based scheme, however, is less effective when the patterns are of comparable densities with the outliers. We then introduce a connectivity-based scheme that improves the effectiveness of the density-based scheme when a pattern itself is of similar density as an outlier. We compare density-based and connectivity-based schemes in terms of their strengths and weaknesses, and demonstrate applications with different features where each of them is more effective than the other. Finally, connectivity-based and density-based schemes are comparatively evaluated on both real-life and synthetic datasets in terms of recall, precision, rank power and implementation-free metrics. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s10115-005-0233-6 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
existing outlier detection scheme,compatibility theory,outlier detection,comparable density,distance-based scheme,large datasets,connectivity-based scheme,credit card fraud detection,computer intrusion,various outlier formulation scheme,density-based scheme,unified model,e commerce | Data mining,Anomaly detection,Credit card fraud,Intrusion,Computer science,Outlier,Credit card,Artificial intelligence,Unified Model,Strengths and weaknesses,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 1 | 0219-3116 |
Citations | PageRank | References |
31 | 1.22 | 23 |
Authors | ||
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 W. Cheung | 4 | 1511 | 156.71 |