Sushilata D. MayanglambamThis email address is being protected from spambots. You need JavaScript enabled to view it.1, 2, Rajendra Pamula1, and Shi-Jinn Horng3

1Department of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad, Jharkhand-826004, INDIA
2Department of Computer Engineering, Mizoram University, Aizawl, Mizoram-796004, INDIA
3Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei-106335, TAIWAN


Received: September 25, 2022
Accepted: December 12, 2022
Publication Date: March 23, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique. Initially, the data clustering of the considered datasets was carried out using PSO to obtain optimized clusters. In the optimization process, we have adopted Davies-Bouldin (DB) index as a fitness function. The optimized clusters were pruned to exclude densely packed inliers data. Thereafter, the KNN method was employed to detect outliers present in the datasets. Our proposed algorithm was tested for outlier detection on eight different datasets and compared its performance with PSO+K-means, K-means, Local Outlier Factor (LOF), and Local Distance-based Outlier Factor (LDOF) methods. Our results show that the outlier detection efficiency of the proposed method outperforms than other four techniques. We believe that our proposed technique simple and efficient in finding the outliers in various types of datasets and it could be a promising tool for outlier detection in data mining.

Keywords: Particle Swarm Optimization; Davies-Bouldin Index; K-Nearest Neighbors; Outlier Detection

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