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Diluvian Clustering: A Fast, Effective Algorithm for Clustering Compositional and Other Data

Published online by Cambridge University Press:  24 August 2015

Nicholas W. M. Ritchie
Nicholas W. M. Ritchie*
Affiliation:
Materials Measurement Science Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899-8372, USA
*
*Corresponding author. nicholas.ritchie@nist.gov
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Abstract

Diluvian Clustering is an unsupervised grid-based clustering algorithm well suited to interpreting large sets of noisy compositional data. The algorithm is notable for its ability to identify clusters that are either compact or diffuse and clusters that have either a large number or a small number of members. Diluvian Clustering is fundamentally different from most algorithms previously applied to cluster compositional data in that its implementation does not depend upon a metric. The algorithm reduces in two-dimensions to a case for which there is an intuitive, real-world parallel. Furthermore, the algorithm has few tunable parameters and these parameters have intuitive interpretations. By eliminating the dependence on an explicit metric, it is possible to derive reasonable clusters with disparate variances like those in real-world compositional data sets. The algorithm is computationally efficient. While the worst case scales as O(N2) most cases are closer to O(N) where N is the number of discrete data points. On a mid-range 2014 vintage computer, a typical 20,000 particle, 30 element data set can be clustered in a fraction of a second.

Keywords

data miningclusteringcompositionEPMAparticle analysis
Type
Equipment and Software Development
Information
Microscopy and Microanalysis , Volume 21 , Issue 5 , October 2015 , pp. 1173 - 1183
Copyright
© Microscopy Society of America 2015 

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Footnotes

a

Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.

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