k-means clustering is a flat clustering technique, which produces only one partition with k clusters. Unlike hierarchical clustering, which does not require a user to determine the number of clusters at the beginning, the k-means method requires this to be determined first. However, k-means clustering is much faster than hierarchical clustering as the construction of a hierarchical tree is very time consuming. In this recipe, we will demonstrate how to perform k-means clustering on the customer dataset.
In this recipe, we will continue to use the customer dataset as the input data source to perform k-means clustering.
Perform the following steps to cluster the customer dataset with ...
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