International Journal of Computer Applications
The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this paper, the authors present an algorithm that provides outlier detection and data clustering simultaneously. The algorithm improves the estimation of centroids of the generative distribution during the process of clustering and outlier discovery.