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Activity Number:
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107
- SPEED: Statistical Methods, Computing, and Applications Part 1
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #322253
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Title:
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The growclusters Package for R
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Author(s):
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Randall Powers* and Wendy Martinez and Terrance D Savitsky
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Companies:
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Bureau of Labor Statistics and Bureau of Labor Statistics and U.S. Bureau of Labor Statistics
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Keywords:
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clustering;
R Shiny;
unsupervised learning;
k-means clustering;
text analysis;
hierarchical clustering
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Abstract:
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The growclusters package for R implements an enhanced version of k-means clustering that allows discovery of local clusterings or partitions for a collection of data sets that each draw their cluster means from a single, global partition. The package contains functions to estimate a partition structure for multivariate data. Estimation is performed under a penalized optimization derived from Bayesian non-parametric formulations. This paper describes some of the functions and capabilities of the growclusters package, including the creation of R Shiny applications designed to visually illustrate the operation and functionality of the growclusters package.
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Authors who are presenting talks have a * after their name.
