October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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Pros and cons of K-means
The advantages of this method are as follows:
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It scales very well (most of the calculations can be run in parallel)
- It has been used in a very large range of applications
But simplicity also has some costs (the no silver bullet rule applies):
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It requires a priori knowledge (the number of possible clusters should be known beforehand)
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The outlier values can skew the values of the centroids, as they have the same weight as any other sample
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As we assume that the figure is convex and isotropic, it doesn’t work very well with non blob alike clusters
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