Unsupervised Way to Choose K in K-Means Algorithm

1. How do you select K in K-means algorithm?
2. How K-means algorithm is used in unsupervised learning?
3. How do you choose K in KModes?

How do you select K in K-means algorithm?

There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.

How K-means algorithm is used in unsupervised learning?

K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.

How do you choose K in KModes?

For KModes, plot cost for a range of K values. Cost is the sum of all the dissimilarities between the clusters. Select the K where you observe an elbow-like bend with a lesser cost value. We can see a bend at K=3 in the above graph indicating 3is the optimal number of clusters.