Abstract
The goal of anomaly detection is to find out the new feature that is different to others. In this paper, we are finding anomalies using a two-step approach. In the first step, the modified k-mean algorithm is used for dividing the data patterns/objects or points into clusters. Data points in the same cluster will be mostly anomalies or all non-anomalies. In second step, constructing a max heap is based on the number of points in the clusters. Points are present in the cluster at the leaf level; they all are distant from the other clusters regarded as anomalies.
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Jain, P.K., Pamula, R. (2019). Two-Step Anomaly Detection Approach Using Clustering Algorithm. In: Kamal, R., Henshaw, M., Nair, P. (eds) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-13-2673-8_54
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DOI: https://doi.org/10.1007/978-981-13-2673-8_54
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