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CLASSIX is a fast and explainable clustering algorithm based on sorting. Here are a few highlights:
* Ability to cluster low and high-dimensional data of arbitrary shape efficiently.
* Ability to detect and deal with outliers in the data.
* Ability to provide textual explanations for the generated clusters.
* Full reproducibility of all tests in the accompanying paper.
* Support of Cython compilation.
``CLASSIX`` is a contrived acronym of CLustering by Aggregation with Sorting-based Indexing and the letter X for explainability. CLASSIX clustering consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data points, followed by a merging phase of groups into clusters. The algorithm is controlled by two parameters, namely the distance parameter radius for the group aggregation and a minPts parameter controlling the minimal cluster size.
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Installing and example
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CLASSIX has the following dependencies for its clustering functionality:
* cython
* numpy
* scipy
* requests
and requires the following packages for data visualization:
* matplotlib
* pandas
To install the current CLASSIX release via PIP use:
.. code:: bash
pip install classixclustering
To check the CLASSIX installation you can use:
.. code:: bash
python -m pip show classixclustering
Download the repository via:
.. code:: bash
git clone https://github.com/nla-group/classix.git
Example usage:
.. code:: python
from sklearn import datasets
from classix import CLASSIX
# Generate synthetic data
X, y = datasets.make_blobs(n_samples=2000000, centers=4, n_features=10, random_state=1)
# Employ CLASSIX clustering
clx = CLASSIX(sorting='pca', verbose=1)
clx.fit(X)
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Citation
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.. code:: bibtex
@techreport{CG22b,
title = {Fast and explainable clustering based on sorting},
author = {Chen, Xinye and G\"{u}ttel, Stefan},
year = {2022},
number = {arXiv:2202.01456},
pages = {25},
institution = {The University of Manchester},
address = {UK},
type = {arXiv EPrint},
url = {https://arxiv.org/abs/2202.01456}
}
