Autors: Karale, A. V., Lazarova, M. K., Koleva, P. H., Poulkov, V. K.
Title: Advanced Memory Efficient Outlier Detection Approach for Streaming Data using Swarm Optimization
Keywords: Data Streaming; LOCI; Outlier Detection; Particle Swarm Optimization; Swarm Intelligence

Abstract: Outlier detection techniques detect abnormal behavior in data and are useful in a variety of applications. In a real-life scenario, various applications generate large-scale data every day. Outlier detection over such continuous/streaming data is a challenging task due to its volume and limitations in processing memory. This paper presents an outlier detection approach called Advanced Memory Efficient Outlier Detection (A-MEOD) that is able to find outliers in streaming data in a memory-efficient manner. The outlier detection is based on the MEOD technique and Local Correlation Integral (LOCI) algorithm. Further the A-MEOD technique reduces the LOCI calculations and finds the top M outliers using Knorr's definition. The results of utilization of A-MEOD are compared with MiLOF and MEOD in terms of accuracy, time, and memory requirements.



    in Proceedings of International Conference on Telecommunications and Signal Processing (TSP), Brno, Czech Republic, 26-28 July 2021, pp. 346-351, 2021, Czech Republic, DOI 10.1109/TSP52935.2021.9522667

    Copyright IEEE

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science