Clustering

Clustering is an unsupervised learning technique in which you group categorically similar data points into batches, called clusters. Here, we will be focusing on the k-means clustering method.

K-means clustering is a clustering algorithm based on iterations where similar data points are grouped into a cluster based on their closeness to the cluster centroid. This means that the model runs iteratively to find the cluster centroid.

The optimum number of clusters for a dataset is found by using the elbow method.

Method to Find the Optimum Number of Clusters

The logic behind k-means clustering is to define a cluster in such a way that, within the cluster, the sum of square (WSS) is minimized. The smaller the value of WSS, the better the compactness of the cluster. The clusters that are compact have data points that are similar to one another. We will be using the elbow method to find the optimum number of clusters.

The elbow method gets its name from the arm...