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broken arbitrarily. This results in a partitioning of the data.  Step 2: Relocation of “means”. Each cluster representative is relocated to the center  (mean) of all data points assigned to it. If the data points come with a probability measure  (weights), then the relocation is to the expectations (weighted mean) of the data partitions. The number of clusters should match the data. An incorrect choice of the number of clusters will invalidate the whole process. An empirical way to find the best number of clusters is to try K-means clustering with different number of clusters and measure the resulting sum of squares