For many machine learning algorithms it is often worth randomising the order of training data before presenting it to the algorithm. For example, if all the examples of a particular class are at the beginning of a data set this might make a clustering algorithm initially create sub-clusters of this first class which may get frozen into the final clusters even when more mixed data is encountered; by randomizing the order this effect is minimised.
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