How do I apply the elbow method for clustering?
The elbow method is used to determine the optimal number of clusters in a dataset. You start by plotting the sum of squared distances from each point to its assigned cluster center as a function of the number of clusters.
What data do I need to perform this method?
You need a dataset that you want to cluster. It should be numerical data, as the method relies on calculating distances.
Can you give me an example of how to implement it in Python?
Sure! You can use libraries like KMeans from scikit-learn. First, fit the model for a range of cluster numbers, then plot the inertia to find the 'elbow' point.
What should I look for in the plot?
Look for a point where the rate of decrease sharply changes, forming an 'elbow'. This point suggests the optimal number of clusters.
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