(This article was first published on

**Analysis with Programming**, and kindly contributed to R-bloggers)Enough with the theory we recently published, let’s take a break and have fun on the application of Statistics used in Data Mining and Machine Learning, the

*k*-Means Clustering.k-means clusteringis a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.k-means clustering aims to partitionnobservations intokclusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. (Wikipedia, Ref 1.)

We will apply this method to an image, wherein we group the pixels into *k* different clusters. Below is the image that we are going to use,

Colorful Bird From Wall321 |

We will utilize the following packages for input and output:

### Download and Read the Image

Let’s get started by downloading the image to our workspace, and tell R that our data is a JPEG file.

### Cleaning the Data

Extract the necessary information from the image and organize this for our computation:

The image is represented by large array of pixels with dimension *rows* by *columns* by *channels* — red, green, and blue or RGB.

### Plotting

Plot the original image using the following codes:

### Clustering

Apply *k*-Means clustering on the image:

Plot the clustered colours:

Possible clusters of pixels on different *k*-Means:

I suggest you try it!

### Reference

- K-means clustering.
*Wikipedia*. Retrieved September 11, 2014.

To

**leave a comment**for the author, please follow the link and comment on their blog:**Analysis with Programming**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...