December 2018
Intermediate to advanced
318 pages
8h 28m
English
Content preview from Hands-On Machine Learning for Cybersecurity
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Choosing k for k-means
There is no algorithm that actually derives the exact value of k to be used in a k-means algorithm. The user runs the k-means for various values of k and finds the one that is optimum. An accurate estimate for k can be made with the following method.
Here, the mean distance is computed between the cluster elements and the cluster centroid. By logic, if we increase the value of k, that is, increase the number of clusters in the data, the number of data points within a cluster decreases. Hence, if the value of k is equal to the number of total data points, the sum squared error is 0, as the centroid is the same as the data point.
Hence, in the elbow method, the errors are plotted for every value of the chosen k and generated ...
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