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Identifying Outliers in Datasets Using Outlier Removal Clustering (ORC) Algorithm

N. Nirmaladevi, R. Suresh Kumar


The objective function of general K-Mean, this work associates a weight vector with each cluster to indicate which dimensions are relevant to the clusters. To prevent the value of the objective function from decreasing because of the elimination of dimensions, virtual dimensions are added to the objective function. The values of data points on virtual dimensions are set artificially to ensure that the objective function is minimized when the real subspace clusters or the clusters in original space are found. 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. This research work presents 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.


Data Mining, Clustering, K-Means, High Dimensions, Outlier Removal Clustering (ORC) Algorithm.

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