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In this study, the authors aim to propose an optimized density-based algorithm for anomaly detection with focus on high-dimensional datasets. The optimization is achieved by optimizing the input parameters of the algorithm using firefly meta-heuristic. The performance of different similarity measures for the algorithm is compared including both L1 and L2 norms to identify the most efficient similarity measure for high-dimensional datasets. The algorithm is optimized further in terms of speeddoi:10.12694/scpe.v19i1.1394 fatcat:36otfgyk6zcj7khzsv5ggjpww4