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 ...

This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented using the C# language. Briefly, the source mixed data is preprocessed ...

This article demonstrates K-means clustering benchmarking as a case study for Spark resource allocation and tuning analysis. Spark K-Means resource tuning: Introduction to K-means clustering. K-Means ...

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.