July 2017
Intermediate to advanced
360 pages
8h 26m
English
Content preview from Machine Learning Algorithms
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Finding the optimal number of clusters
One of the most common disadvantages of k-means is related to the choice of the optimal number of clusters. An excessively small value will determine large groupings that contain heterogeneous elements, while a large number leads to a scenario where it can be difficult to identify the differences among clusters. Therefore, we're going to discuss some methods that can be employed to determine the appropriate number of splits and to evaluate the corresponding performance.
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