Dechipering K-Means Algorithm

The k-means is a simple algorithm that divides the data set into k partitions for n objects where k ≤ n. In this method, the data set is partitioned into homogeneous groups with similar characteristics. The similarity or dissimilarity is defined by calculating the distance between the centroid and data points. The clusters are formed when the optimization function for the algorithm achieves its objective — less intracluster distances and more intercluster distances.

The following steps are involved in traditional k-means clustering:

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