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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pei Sun Chawla, S. |
| Copyright Year | 2004 |
| Description | Author affiliation: Sch. of Inf. Technol., Sydney Univ., NSW, Australia (Pei Sun) |
| Abstract | We propose a measure, spatial local outlier measure (SLOM) which captures the local behaviour of datum in their spatial neighborhood. With the help of SLOM, we are able to discern local spatial outliers which are usually missed by global techniques like "three standard deviations away from the mean". Furthermore, the measure takes into account the local stability around a data point and supresses the reporting of outliers in highly unstable areas, where data is too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets which show that our approach is scalable to large data sets. |
| Sponsorship | IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI) |
| Starting Page | 209 |
| Ending Page | 216 |
| File Size | 280572 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769521428 |
| DOI | 10.1109/ICDM.2004.10097 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-11-01 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ocean temperature Sea surface Australia Information technology Sea measurements Data mining Chebyshev approximation Sun Area measurement Stability |
| Content Type | Text |
| Resource Type | Article |
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