K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal ...

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 ...

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 ...

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 ...

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized ...

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