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- W3137345384 abstract "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." @default.
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- W3137345384 date "2021-03-12" @default.
- W3137345384 modified "2023-09-27" @default.
- W3137345384 title "MEOD: Memory-Efficient Outlier Detection on Streaming Data" @default.
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- W3137345384 doi "https://doi.org/10.3390/sym13030458" @default.
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