Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative ...

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Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in ...

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This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented using the C# language. Briefly, the source mixed data is preprocessed ...