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 ...

In practice, self-organizing map clustering is often used to validate k-means clustering. A relatively unexplored idea is to perform SOM clustering using an r-by-c map instead of a 1-by-k map.

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to ...

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 ...

Reduced k-means clustering is a method for clustering objects in a low-dimensional subspace. The advantage of this method is that both clustering of objects and low-dimensional subspace reflecting the ...