The k-means clustering algorithm with k-means++ initialization is relatively simple, easy to implement, and effective. One disadvantage of k-means clustering is that it only works with strictly ...

But clustering mixed categorical and numeric data is very tricky. This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented ...

By using K-Means clustering, an online retailer may identify that its client base naturally divides into three groups: budget-conscious shoppers, regular shoppers, and luxury shoppers.