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Adapting the Right Measures for K-Means Clustering

Adapting the Right Measures for K-Means Clustering

This video was recorded at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009. Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the performance of clustering algorithms, these measures often provide inconsistent information about the clustering performance and the best suitable measures to use in practice remain unknown. This paper thus fills this crucial void by giving an organized study of 16 external validation measures for K-means clustering. Specifically, we first introduce the importance of measure normalization in the evaluation of the clustering performance on data with imbalanced class distributions. We also provide normalization solutions... Show More

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