Svetlana Cherednichenko

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We present an Outlier Removal Clustering (ORC) algorithm that provides outlier detection and data clustering simultaneously. The method employs both clustering and outlier discovery to improve estimation of the centroids of the generative distribution. The proposed algorithm consists of two stages. The first stage consist of purely K-means process, while(More)
K-means is considered as one of the most common and powerful algorithms in data clustering, in this paper we're going to present new techniques to solve two problems in the K-means traditional clustering algorithm, the 1 problem is its sensitivity for outliers, in this part we are going to depend on a function that will help us to decide if this object is(More)
Rising sea levels, an effect of global warming, is a cause of concern and it is likely to affect the developing countries. With respect to the data set published for research at the World Bank, clustering a data mining technique is applied to detect the most likely to be affected regions. When tested with the k-Means clustering technique, the result of the(More)
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