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WSS Analysis in K-Means Clustering by Varun Bhayana is a document available to read on EtoBox.

What is WSS Analysis in K-Means Clustering about?

The document discusses how the within sum of squares (WSS) changes as the number of clusters (K) increases in k-means clustering. As K increases from 1 to 5, representing the 5 observations: - WSS drops significantly from K=1 to K=2, indicating 2 is a good number of clusters - The drop is less from K=2 to K=3 as one cluster only has one observation - Further increases in K from 3 to 5 result in smaller drops or no change in WSS, as each additional cluster only contains a single observation

Author

Varun Bhayana

Language

EN