Anomaly detection using a self-organizing map and particle swarm optimization

Abstract Self-Organizing Maps (SOMs) are among the most well-known, unsupervised neural network approaches to clustering, which are very efficient in handling large and high dimensional datasets. The original Particle Swarm Optimization (PSO) is another algorithm discovered through simplified social model simulation, which is effective in nonlinear optimization problems and easy to implement. In the present study, we combine these two methods and introduce a new method for anomaly detection. A discussion about our method is presented, its results are compared with some other methods and its advantages over them are demonstrated. In order to apply our method, we also performed a case study on forest fire detection. Our algorithm was shown to be simple and to function better than previous ones. We can apply it to different domains of anomaly detection. In fact, we observed our method to be a generic algorithm for anomaly detection that may need few changes for implementation in different domains.

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