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
To install the package from CRAN:
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
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.