Finding reproducible cluster partitions for the k-means algorithm


K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure… (More)
DOI: 10.1186/1471-2105-14-S1-S8


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