September 2015
Beginner to intermediate
608 pages
13h 43m
English
Content preview from Clojure for Data Science
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The drawbacks of k-means
k-means is one of the most popular clustering algorithms due to its relative ease of implementation and the fact that it can be made to scale well to very large datasets. In spite of its popularity, there are several drawbacks.
k-means is stochastic, and does not guarantee to find the global optimum solution for clustering. In fact, the algorithm can be very sensitive to outliers and noisy data: the quality of the final clustering can be highly dependent on the position of the initial cluster centroids. In other words, k-means will regularly discover a local rather than global minimum.
The preceding diagram illustrates how ...
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