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.

References

    Issue

    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