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The Self-Updating Process Clustering Algorithms
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README.md

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This package implements the self-updating process clustering algorithms proposed by (Shiu and Chen 2016). This document shows how to reproduce the examples and figures in the paper.

According to the paper, The Self-Updating Process (SUP) is a clustering algorithm that stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering. It is an iterative process on the sample space and allows for both time-varying and time-invariant operators.

The paper shows that SUP is particularly competitive for:

  • Data with noise
  • Data with a large number of clusters
  • Unbalanced data

Installation

To build the package from source, the Windows user requires Rtools and the Mac OS X user requires gfortran.

To install the package from CRAN:

install.packages("supc")

To get the current development version from github:

# install.packages('remotes')
remotes::install_github("wush978/supc")

For details, please visit http://rpubs.com/wush978/supc

Reference

Shiu S and Chen T (2016). “On the strengths of the self-updating process clustering algorithm.” Journal of Statistical Computation and Simulation, 86(5), pp. 1010-1031. doi: 10.1080/00949655.2015.1049605, http://dx.doi.org/10.1080/00949655.2015.1049605, http://dx.doi.org/10.1080/00949655.2015.1049605.

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