Clustering for Data Mining: A Data Recovery Approach

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CRC Press, 2005 M04 29 - 296 páginas
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that wou

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