Machine Learning Algorithms - Second Edition

Mini-batch K-means

The first approach we are going to consider is a mini-batch version of the standard K-means algorithm. In this case, we cannot compute the centroids for all samples, and so the main problem is to define a criterion to reassign the centroids after a partial fit. The standard process is based on a streaming average, and therefore there will be centroids with a higher sample count and others with lower values. In scikit-learn, the fine-tuning of this process is achieved using the reassigment_ratio parameter (whose default value is 0.01). Small values (for example, 0.0001) will let the centroids whose clusters have a fewer number of samples to the same value for a longer time, yielding a sub-optimal but faster solution. On ...