Unsupervised clustering algorithm for N-dimensional data.

Erwin B Montgomery; He Huang; Amir Assadi

doi:10.1016/j.jneumeth.2004.10.015

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Unsupervised clustering algorithm for N-dimensional data.

Erwin B Montgomery·He Huang·Amir Assadi

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Abstract

Cluster analysis is an important tool for classifying data. Established techniques include k-means and k-median cluster analysis. However, these methods require the user to provide a priori estimations of the number of clusters and their approximate location in the parameter space. Often these estimations can be made based on some prior understanding about the nature of the data. Alternatively, the user makes these estimations based on visualization of the data. However, the latter is problematic in data sets with large numbers of dimensions. Presented here is an algorithm that can automatically provide these estimates without human intervention based on the inherent structure of the data set. The number of dimensions does not limit it.

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Algorithms Cluster Analysis Computer Simulation Data Interpretation, Statistical Sensitivity and Specificity Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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Erwin B Montgomery, He Huang, Amir Assadi. 2004-12-18. Unsupervised clustering algorithm for N-dimensional data.. https://doi.org/10.1016/j.jneumeth.2004.10.015

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