Autors: Karale, A. V., Lazarova, M. K., Koleva, P. H., Poulkov, V. K.
Title: MEOD: Memory-efficient outlier detection on streaming data
Keywords: Data streaming; Memory efficiency; Outlier detection; Partic

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.



    Symmetry, vol. 13, issue 3, 2021, Switzerland, Multidisciplinary Digital Publishing Institute (MDPI), DOI 10.3390/sym13030458

    Copyright MDPI

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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science