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A Contrast Pattern Based Clustering Algorithm For Categorical Data, Neil Koberlein Fore

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The data clustering problem has received much attention in the data mining, machine learning, and pattern recognition communities over a long period of time. Many previous approaches to solving this problem require the use of a distance function. However, since clustering is highly explorative and is usually performed on data which are rather new, it is debatable whether users can provide good distance functions for the data. This thesis proposes a Contrast Pattern based Clustering (CPC) algorithm to construct clusters without a distance function, by focusing on the quality and diversity/richness of contrast patterns that contrast the clusters in a …