| 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. References Issue
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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
