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| Content Provider | ACM Digital Library |
|---|---|
| Author | Breunig, Markus M. Sander, Jörg Ng, Raymond T. Kriegel, Hans-Peter |
| Abstract | For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical. |
| Starting Page | 93 |
| Ending Page | 104 |
| Page Count | 12 |
| File Format | |
| ISSN | 01635808 |
| DOI | 10.1145/335191.335388 |
| Journal | ACM SIGMOD Record (SGMD) |
| Volume Number | 29 |
| Issue Number | 2 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1999-06-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Database mining Outlier detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems Software |
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