Abstract
The main aim of this paper is to detect anomaly in the dataset using the technique Outlier Removal Clustering (ORC) on IRIS dataset. This ORC technique simultaneously performs both K-means clustering and outlier detection. We have also shown the working of ORC technique. The datapoints which is far away from the cluster centroid are considered as outliers. The outliers affect the overall performance and result so the focus is on to detect the outliers in the dataset. Here, we have adopted the preprocessing technique to handle the missing data and categorical variable to get the accurate output. To select the initial centroid we have used Silhouette Coefficient.
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Sarvani, A., Venugopal, B., Devarakonda, N. (2019). Anomaly Detection Using K-means Approach and Outliers Detection Technique. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_35
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DOI: https://doi.org/10.1007/978-981-13-0589-4_35
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