K means Clustering

K-means clustering is one of the most commonly used unsupervised machine learning algorithms for partitioning a given data set into a set of k groups (clusters), where k represents the number of groups pre-specified by the analyst. It classifies objects in multiple groups, such that objects within the same cluster are as similar as possible (high intra-class similarity), whereas objects from different clusters are as dissimilar as possible (low inter-class similarity). In k-means Clustering, each group is represented by its centroid (center), which corresponds to the mean of points assigned to the cluster.

K-Means Algorithm steps:

K-Means Syntax:

Example:

In this section, we will step by step go through a complete K means Clustering using USArrests dataset.

Graph Output:

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K means Clustering

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