https://github.com/zcebeci/vatana
Visual Assessment of Clustering Tendency for Finding the Number of Clusters in Datasets
https://github.com/zcebeci/vatana
assessment binary-image cluster cluster-analysis clustering clustering-algorithm clustering-analysis clustering-evaluation clustering-methods count-blocks dissimilarity distance-matrix fuzzy-clustering k-means-clustering number-of-clusters partitional-clustering partitioning soft-clustering vat visual
Last synced: 11 days ago
JSON representation
Visual Assessment of Clustering Tendency for Finding the Number of Clusters in Datasets
- Host: GitHub
- URL: https://github.com/zcebeci/vatana
- Owner: zcebeci
- License: gpl-3.0
- Created: 2015-07-12T19:30:43.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2019-12-11T08:20:45.000Z (over 5 years ago)
- Last Synced: 2023-03-02T21:17:24.663Z (over 2 years ago)
- Topics: assessment, binary-image, cluster, cluster-analysis, clustering, clustering-algorithm, clustering-analysis, clustering-evaluation, clustering-methods, count-blocks, dissimilarity, distance-matrix, fuzzy-clustering, k-means-clustering, number-of-clusters, partitional-clustering, partitioning, soft-clustering, vat, visual
- Language: R
- Homepage:
- Size: 198 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# VatAna
## *Visual Assessment of Clustering Tendency for Finding the Number of Clusters in a Dataset*
`VatAna` is an R package which is an implementation of the *Visual Assessment of Cluster Tendency (VAT)* algorithm proposed by Bezdek & Hathaway (2002).
## Introduction
The partitioning algorithms require a priori estimate of number of clusters (`k`) as an input parameter, and thus the success of partitioning depends mostly on this parameter. In order to find an optimal estimation of `k`, the obtained results are checked by the cluster validity indices at the end of each run of successive cluster analyses. Unfortunately, this kind of cluster validation is time consuming task, and also depends on the clustering indices which may not guarantee the quality of clustering since their performances vary with complexity in data structures. In order to find an optimal number of clusters in datasets, one can benefit from the preprocessing approaches like visual assessment of clustering tendency algorithm before going to clustering session. The visual assessment of clustering tendency (VAT) is a frontier algorithm which produces a gray-level image of reordered distance matrix showing existing clusters with dark blocks along the diagonal of it. This R package provides various functions related with VAT analysis and demonstrates its usage with the examples.
## Install the package 'VatAna'
In order to install the package `VatAna` from the GitHub repository you should first install the `devtools` package from CRAN into your local system. Then you can install the package `VatAna` using `install_github` of `devtools` package as in the R code chunks below:
```{r}
if(!require(devtools)) {install.packages('devtools'); library(devtools)}
install_github("zcebeci/VatAna")
```
If you would like to have a compiled version of the vignettes of the package try to install the package `VatAna` using `install_github` with `build_vignettes` argument set to TRUE as shown below:
```{r}
if(!require(devtools)) {install.packages('devtools'); library(devtools)}
devtools::install_github("zcebeci/VatAna", build_vignettes=TRUE)
```
If you have not already installed `rmarkdown` and `prettydoc` in your local system, before running the above install commands firstly install these packages as following:
```{r}
install.packages('prettydoc')
```
## Load the package into R working space
After you installed the package `VatAna`, run the following command in order to load it to R working space.
```{r}
library(VatAna)
```
## Help for the package
To get help about the use of functions included in the package `VatAna`, run `help` in R as seen in the following code chunk.
```{r}
help(package="VatAna")
```
For reaching the detailed vignette about the usage of package with examples, run the following command and then click *HTML* link on the accessed page in your web browser. Read the vignette and apply the examples.
```{r}
browseVignettes("VatAna")
```
## Cite the package
To cite the package please use one of the following items fits to your references list:
Cebeci, Z. & Yildiz, F. (2015). Görsel Kümelenme Eğilimi Değerlendirmesi ve R'de Uygulaması. *Çukurova Üniversitesi Ziraat Fakültesi Dergisi*, Vol. 30, No. 2, pp. 1-8.
(URL: [https://dergipark.org.tr/en/download/article-file/219860](https://dergipark.org.tr/en/download/article-file/219860))
or in BibTeX format:
```
@article{cebeci30gorsel,
title={G{\"o}rsel K{\"u}melenme E{\u{g}}ilimi De{\u{g}}erlendirmesi ve R’de Uygulamas{\i}},
author={Cebeci, Zeynel and Yildiz, Figen},
journal={{\c{C}}ukurova {\"U}niversitesi Ziraat Fak{\"u}ltesi Dergisi},
volume={30},
number={2},
pages={1--8}
}
```