cnnclustering 0.5.0
A Python package for common-nearest-neighbours clustering
Keywords: Density-based clustering

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Common-nearest-neighbours clustering
====================================

***
**NOTE**

*This project is currently under development.*
*The implementation may change in the future. Check the examples and the documentation for up-to-date information.*

***

cnnclustering
-------------


The `cnnclustering` Python package provides a flexible interface to use the <b>c</b>ommon-<b>n</b>earest-<b>n</b>eighbours cluster algorithm. While the method can be applied to arbitrary data, this implementation was made before the background of processing trajectories from Molecular Dynamics simulations. In this context the cluster result can serve as a suitable basis for the construction of a core-set Markov-state (cs-MSM) model to capture the essential dynamics of the underlying molecular processes. For a tool for cs-MSM estimation, refer to this separate [project](https://github.com/janjoswig/cs-MSM).

The package provides a main module:

  - `cluster`: User interface to (hierarchical) common-nearest-neighbours clustering

Further, it contains the modules:

  - `plot`: Convenience functions to evaluate cluster results
  - `_types`: Direct access to generic types representing needed cluster components
  - `_fit`: Direct access to generic clustering procedures

Features:

  - Flexible: Clustering can be done for data sets in different input formats. Easy interfacing with external methods.
  - Convenient: Integration of functionality, handy in the context of Molecular Dynamics.
  - Fast: Core functionalities implemented in Cython.

Please refer to the following papers for the scientific background (and consider citing if you find the method useful):

  - B. Keller, X. Daura, W. F. van Gunsteren *J. Chem. Phys.*, __2010__, *132*, 074110.
  - O. Lemke, B.G. Keller *J. Chem. Phys.*, __2016__, *145*, 164104.
  - O. Lemke, B.G. Keller *Algorithms*, __2018__, *11*, 19.

Documentation
-------------

The package documentation (under developement) is available [here](https://janjoswig.github.io/CommonNNClustering/) online or under `docs/index.html`.
The sources for the documentation can be found under `docsrc/`.

Install
-------

Refer to the [documentation](https://janjoswig.github.io/CommonNNClustering/_source/install.html) for more details. Install from PyPi

```bash
$ pip install cnnclustering
```

or clone the development version and install from a local branch

```bash
$ git clone https://github.com/janjoswig/CommonNNClustering.git
$ cd CommonNNClustering
$ pip install .
```

Quickstart
----------

```python
>>> from cnnclustering import cluster

>>> # 2D data points (list of lists, 12 points in 2 dimensions)
>>> data_points = [   # point index
...     [0, 0],       # 0
...     [1, 1],       # 1
...     [1, 0],       # 2
...     [0, -1],      # 3
...     [0.5, -0.5],  # 4
...     [2,  1.5],    # 5
...     [2.5, -0.5],  # 6
...     [4, 2],       # 7
...     [4.5, 2.5],   # 8
...     [5, -1],      # 9
...     [5.5, -0.5],  # 10
...     [5.5, -1.5],  # 11
...     ]

>>> clustering = cluster.Clustering(data_points)
>>> clustering.fit(radius_cutoff=1.5, cnn_cutoff=1, v=False)
>>> clustering.labels
array([1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2])

```

![quickstart](https://github.com/janjoswig/CommonNNClustering/raw/master/docs/_images/tutorial_basic_usage_30_0.png)

Alternative scikit-learn implementation
---------------------------------------

We provide an alternative approach to common-nearest-neighbours clustering in the spirit of the scikit-learn project within [scikit-learn-extra](https://github.com/scikit-learn-contrib/scikit-learn-extra).