This is a Fuzzy-k-Means implementation according to Ross T. (2010), Fuzzy Logic with Engineering Applications, p.152-153 written in Scala with a scikit-learn-like API.
To run the examples I recommend to use the Docker image shokuninsan/fuzzylab that I have prepared just for you. So make sure you have Docker installed on your machine.
Run a Docker container from within the root of this project with docker run -i -t -p 0.0.0.0:8888:8888 -v $(pwd):/home/pylab shokuninsan/fuzzylab. On success you get a shell within the container (you will notice that your command prompt has changed, e.g. root@cc85df33e59c:/#).
Within the docker container change into the mounted volume cd home/pylab/.
From there you can run the IPython notebook: ipython notebook --ip=0.0.0.0 --port=8888.
To work with the notebook you need to access the webserver via your browser on your host system. To obtain the appropriate IP address of your container on Mac OS X, you need to execute docker-machine ip default. Now you can navigate your browser to <your-ip>:8888/tree/examples/visualization, select iris.ipynb and in the menu bar click Cell > Run All.
If you want to trigger some knobs on KMeans (e.g. change fuzziness or numClusters parameters), just open the iris.amm script within the IPython notebook in your browser, change it as you desire and re-run the related cells in iris.ipynb.
Note: you can reconnect to a running docker container like this
$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
cc85df33e59c plotlab "/bin/bash" 8 hours ago Up 8 hours 0.0.0.0:8000-8001->8000-8001/tcp dreamy_joliot
$ docker exec -i -t cc85df33e59c bash