Looking for 2D artificial data to demonstrate properties of clustering algorithms

Question

I am looking for datasets of 2 dimensional datapoints (each datapoint is a vector of two values (x,y)) following different distributions and forms ? Or is there any codes to generate such a datapoints ? I want to use them to plot/visualise how some clustering algorithms performs on them. Here are some examples:

• star like cloud data
• four clusters, one easy seperable
• a spiral (no cluster)
• a ring
• two barely seperated clouds
• two parallel clusters forming a spiral
• ... etc

Answer 1

R comes with a lot of datasets, and it looks like it would not be a big deal to reproduce most of the examples you cited with few lines of code. You may also find the mlbench package useful, in particular synthetic datasets starting with mlbench.*. Some illustrations are given below.

You will find additional examples by looking at the Cluster Task View on CRAN. For example, the fpc package has a built-in generator for "face-shaped" clustered benchmark datasets (rFace).

Similar considerations apply to Python, where you will find interesting benchmark tests and datasets for clustering with the scikit-learn.

The UCI Machine Learning Repository hosts a lot of datasets as well, but you're better off simulating data yourself with the language of your choice.

Answer 2

Here are some datasets designed exactly for this task:

The Fundamental Clustering Problem Suite by Ultsch

Answer 3

Here is a customizable cluster generator. It only addresses a certain class of data sets, but it can surely be used for cluster algorithm investigations.

Here is an example of the kind of clusters it can create:

Cluster affiliation is saved in a text file. The code is open source under MIT license.

Answer 4

ELKI comes with a couple of data sets (check also the unit tests, they contain many more than those on the web site, along with parameter settings).

It also includes a fairly flexible data generator.

Answer 5

I can't believe that nobody has mentioned Fisher's Iris data.

I don't think I've seen a clustering technique that doesn't use the iris data as an example.

In r, just type "iris" to access the data.

Here's an example of a nice (and typical) iris plot: http://ygc.name/2011/12/24/ml-class-7-kmeans-clustering/