https://github.com/wush978/supc

The Self-Updating Process Clustering Algorithms
https://github.com/wush978/supc

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The Self-Updating Process Clustering Algorithms

• Host: GitHub
• URL: https://github.com/wush978/supc
• Owner: wush978
• Created: 2016-09-12T05:24:56.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2021-12-10T14:50:28.000Z (almost 3 years ago)
• Last Synced: 2023-11-20T14:43:17.944Z (12 months ago)
• Language: R
• Size: 3.56 MB
• Stars: 9
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        

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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](http://cran.csie.ntu.edu.tw/bin/windows/Rtools/) and the Mac OS X user requires [gfortran](http://cran.csie.ntu.edu.tw/bin/macosx/tools/).

To install the package from CRAN:

```r

install.packages("supc")

```

To get the current development version from github:

```r

# install.packages('remotes')

remotes::install_github("wush978/supc")

```

For details, please visit 

# 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.