K-Means Clustering with Scikit-Learn
K-means clustering is one of the most widely used unsupervised machine learning algorithms that forms clusters of data based on the similarity between data instances. For this particular algorithm to work, the number of clusters has to be defined beforehand. The K in the K-means refers to the number of clusters.
The K-means algorithm starts by randomly choosing a centroid value for each cluster. After that the algorithm iteratively performs three steps:
(i) Find the Euclidean distance between each data instance and centroids of all the clusters;
(ii) Assign the data instances to the cluster of the centroid with nearest distance;
(iii) Calculate new centroid values based on the mean values of the coordinates of all the data instances from the corresponding cluster.
A Simple Example
Let's try to see how the K-means algorithm works with the help of a handcrafted example, before implementing the algorithm in Scikit-Learn.
We have a set of the following two dimensional data instances named D.
D = { (5,3), (10,15), (15,12), (24,10), (30,45), (85,70), (71,80), (60,78), (55,52), (80,91) }
# We want to divide this data into two clusters,
# C1 and C2 based on the similarity between the data points.The first step is to randomly initialize values for the centroids of both clusters. Let's name centroids of clusters C1 and C2 as c1 and c2 and initialize them with the values of the first two data points
i.e. (5, 3) and (10, 15).