A new outlier detection algorithm based on observation-point mechanism

Abstract

Outlier detection is an important branch of data mining research, and has wide applications in the fields of finance, telecommunications, and biology. The traditional nearest neighbor-based outlier detection (NNOD) and local outlier factor-based outlier detection (LOFOD) algorithms generally have high computational complexity and high false-detection rates. This paper proposes an observation-point mechanism-based outlier detection (OPOD) algorithm comprising four core steps: i) generating random observation points in the original data space; ii) estimating the probability density function of distance values between the given observation point and all data points; iii) calculating the probabilities of distance values for the given observation point; and iv) detecting outliers by combining the probabilities corresponding to the different observation points. Extensive experiments are conducted to demonstrate the feasibility, rationality, and effectiveness of the OPOD algorithm. The experimental results show that the OPOD algorithm converges as the number of observation points increases, and can attain better detection performance with lower computation complexity than the NNOD and LOFOD algorithms.