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This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.

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milaan9/Clustering-Datasets

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Clustering-Datasets

Artificial data

2d-10c 2d-20c-no0 2d-3c-no123 2d-4c-no4 2d-4c-no9 2d-4c 2sp2glob 3-spiral 3MC D31 DS577 DS850 R15 aggregation atom banana birch-rg1 birch-rg2 birch-rg3 chainlink cluto-t4.8k cluto-t5.8k cluto-t7.10k cluto-t8.8k complex8 complex9 compound cure-t0-2000n-2D cure-t1-2000n-2D cure-t2-4k curves1 curves2 dartboard1 dartboard2 dense-disk-3000 dense-disk-5000 diamond9 disk-1000n disk-3000n disk-4000n disk-4500n disk-4600n disk-5000n disk-6000n donut1 donut2 donut3 donutcurves ds2c2sc13 ds3c3sc6 ds4c2sc8 elliptical_10_2 elly-2d10c13s engytime flame fourty golfball hepta insect jain long1 long2 long3 longsquare lsun mopsi-finland mopsi-joensuu pathbased rings s-set1 s-set2 s-set3 s-set4 sizes1 sizes2 sizes3 sizes4 sizes5 smile1 smile2 smile3 spherical_4_3 spherical_5_2 spherical_6_2 spiral spiralsquare square1 square2 square3 square4 square5 st900 target tetra triangle1 triangle2 twenty twodiamonds wingnut xclara zelnik1 zelnik2 zelnik3 zelnik4 zelnik5 zelnik6

Frequently asked questions ❔

How can I thank you for creating and sharing this repository? 🌷

You can and Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click ➞ ✰ Star and ⵖ Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

How can I use these datasets without an Internet connection?

Download ZIP

  1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.
  2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.

Authors ✍️

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a ⭐ star.

Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.