Today I read a little bit about Elbow Method:
From what I understand, in unsupervised algorithms, it’s vital to find the right number of clusters for your data, which isn’t predefined. We use methods like the Elbow Method in K-Means clustering to make this determination.
To find the optimal K, you iterate from K=1 to K=n (where n is a chosen parameter) and calculate the within-cluster sum of squares (WCSS) for each K. WCSS is the sum of squared distances between the centroids and each data point. In simpler words, it is basically how “tidy” our clusters would be
To determine the best K, you create a graph of K against its corresponding WCSS values. Interestingly, this graph often resembles an elbow. At K=1, WCSS is highest, but as K increases, WCSS decreases. The optimal K is usually where the graph starts to straighten out, indicating a point of diminishing returns.