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MEOD: Memory-Efficient Outlier Detection on Streaming Data
| Content Provider | MDPI |
|---|---|
| Author | Karale, Ankita Lazarova, Milena Koleva, Pavlina Poulkov, Vladimir |
| Copyright Year | 2021 |
| Description | In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points defined by a given radius. The radius value detection problem is converted into an optimization problem. The radius value is determined using a particle swarm optimization (PSO)-based approach. The results of the MEOD technique application are compared with existing approaches in terms of memory, time, and accuracy, such as the memory-efficient incremental local outlier factor (MiLOF) detection technique. The MEOD technique finds outlier points similar to MiLOF with nearly equal accuracy but requires less memory for processing. |
| Starting Page | 458 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym13030458 |
| Journal | Symmetry |
| Issue Number | 3 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-12 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Computation Theory and Mathematics Outlier Detection Data Streaming Memory Efficiency Particle Swarm Optimization Swarm Intelligence |
| Content Type | Text |
| Resource Type | Article |
