How to implement K means Clustering in shogun
In this recipe, we shall go through the steps that will help us understand how to implement K means Clustering in shogun.
Recipe Objective: How to implement K-means Clustering in shogun?
This recipe explains how to implement K-means Clustering in shogun.
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Learn to Implement Customer Churn Prediction Using Machine Learning in Python
K-means Clustering
K mean cluster separates n number of observations into k<=n clusters where every observation is part of a cluster with the closest mean serving as a cluster prototype.
x_train = RealFeatures(f_train)
d = EuclideanDistance(x_train, x_train)
k = KMeans(3, d)
k.train()
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