A step towards statistical modelling…
K-means Clustering algorithm is popular unsupervised machine learning algorithms, that solve the well-known clustering problem, with no pre-determined labels defined. It is often referred to as Lloyd’s algorithm.
The K-means clustering algorithm, tries to find out K number of centroids, and then allocates every data point to the nearest cluster. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid. K refers to the total number of clusters to be defined in the entire dataset.
Algorithm:
- Step1: Randomly initialize the cluster centres of each cluster from the data points.
- Step2: For each dataset point, compute the euclidian distance from all the centroids and assign the cluster to it’s nearest centroid.
- Step3: Recompute centroids, by taking the average of all the data points which belong to that cluster
- Step4: Repeat the previous two steps, until there are no more changes of data points belonging to clusters.
Implementation
1) Dataset Creation:
2) Initialize Cluster Centers:
3) Compute Euclidian distance
4) Recompute centroids
5) Repeat till Convergence
6) Plot Results
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