A swarm intelligence based clustering approach for outlier detection

Outlier detection is an important field in data mining and knowledge discovery, which aims to identify abnormal observations in a large dataset. Common application areas of outlier detection are intrusion detection in computer networks, credit cards fraud detection, detecting abnormal changes in stock prices, and identifying abnormal health conditions. We propose the use of a novel swarm intelligence based clustering technique called Hierarchical Particle Swarm Optimization Based Clustering (HPSO-clustering) for outlier detection. The proposed technique is able to perform Hierarchical Agglomerative Clustering (HAC) as well as outlier detection. In the proposed approach a swarm of particles evolves through different stages to identify outliers and normal clusters. The experimentation of the proposed approach is performed on benchmark datasets which show that the efficiency of the approach is better than some other popular outlier detection techniques.