Data clustering, or cluster analysis, is the process of grouping data items so that similar items belong to the same group/cluster. There are many clustering techniques. In this article I'll explain ...

Colour quantization, the process of reducing the number of distinct colours in an image while maintaining visual fidelity, is a cornerstone of digital image processing and computer graphics. Rooted in ...

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 ...

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 ...

In this paper, the authors contain a partitional based algorithm for clustering high-dimensional objects in subspaces for iris gene dataset. In high dimensional data, clusters of objects often exist ...

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 ...

K-means is comparatively simple and works well with large datasets, but it assumes clusters are circular/spherical in shape, so it can only find simple cluster geometries. Data clustering is the ...

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 ...