A Rough set outlier detection based on Particle Swarm Optimization

Outlier is strange data values that stand out from datasets. In some applications, finding outliers are more interesting than finding inliers in datasets, such as fraud detection, network system, financial and others. In this research, an algorithm is proposed to find minimum non-Reduct based on Rough set using Particle Swarm Optimization (PSO) for outlier detection. Like Genetic Algorithm (GA), PSO is also a type of optimization algorithm based on populations. It requires only simple mathematical operator and computationally inexpensive in terms of both memory and time. The experiment has been carried out to compute the performance between PSO and GA using 10 UCI datasets and 2 data networks. The comparisons shown that PSO has the ability to detect outliers, with inexpensive computation time compared to GA.