When using any clustering algorithm, including k-means, you should normalize the data so that all the columns have roughly the same range, typically between 0 and 1, or between -1 and +1. This ...

The K-Means Algorithm The k-means algorithm, sometimes called Lloyd's algorithm, is simple and elegant. The algorithm is illustrated in Figures 3-7. In pseudo-code, k-means is: initialize clustering ...

Then, you can use clustering results to custom tailor your marketing efforts. In this course, we will explore two popular clustering techniques: Agglomerative hierarchical clustering and K-means ...

A key step in deploying clustering is deciding which algorithm to use. One of the most common is k-means, which works by computing the “distances” (i.e., similarity) between data points and ...

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 ...

In the proposed algorithm, they extend the K-Means clustering process to calculate a weight for each dimension in each cluster and use the weight values to identify the subsets of important ...

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.