Business Intelligence

DCBOR: A Density Clustering Based on Outlier Removal

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Executive Summary

Data clustering is an important data exploration technique with many applications in data mining. The authors present an enhanced version of the well known single link clustering algorithm. They will refer to this algorithm as DCBOR. The proposed algorithm alleviates the chain effect by removing the outliers from the given dataset. So this algorithm provides outlier detection and data clustering simultaneously. This algorithm does not need to update the distance matrix, since the algorithm depends on merging the most k-nearest objects in one step and the cluster continues grow as long as possible under specified condition. So the algorithm consists of two phases; at the first phase, it removes the outliers from the input dataset. At the second phase, it performs the clustering process.

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