This report focuses on how to tune a Spark application to run on a cluster of instances. We define the concepts for the cluster/Spark parameters, and explain how to configure them given a specific set ...

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 ...

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 assumes you have intermediate or better programming skill but doesn't assume you know anything about k-means clustering. The demo is implemented using C#, but you should be able to ...

The upcoming release of Tableau 10 will introduce new features aimed at simplifying how customers use advanced analytic functions upon their data, such as a new k-means clustering algorithm that works ...

Data clustering is the process of placing data items into different groups (clusters) in such a way that items in a particular group are similar to each other and items in different groups are ...