graph-based-clustering

graph-based-clustering copied to clipboard

Graph-Based Clustering

Graph-Based Clustering using connected components and minimum spanning trees.

Both clustering methods, supported by this library, are transductive - meaning they are not designed to be applied to new, unseen data.

Installation

To install graph-based-clustering run:

pip install graph-based-clustering

Usage

The library has sklearn-like fit/fit_predict interface.

ConnectedComponentsClustering

This method computes pairwise distances matrix on the input data, and using threshold (parameter provided by the user) to binarize pairwise distances matrix makes an undirected graph in order to find connected components to perform the clustering.

Required arguments:

• threshold - paremeter to binarize pairwise distances matrix and make undirected graph

Optional arguments:

• metric - sklearn.metrics.pairwise_distances parameter (default: "euclidean")
• n_jobs - sklearn.metrics.pairwise_distances parameter (default: None)

Example:

import numpy as np
from graph_based_clustering import ConnectedComponentsClustering

X = np.array([[0, 1], [1, 0], [1, 1]])

clustering = ConnectedComponentsClustering(
    threshold=0.275,
    metric="euclidean",
    n_jobs=-1,
)

clustering.fit(X)
labels_pred = clustering.labels_

# alternative
labels_pred = clustering.fit_predict(X)

SpanTreeConnectedComponentsClustering

This method computes pairwise distances matrix on the input data, builds a graph on the obtained matrix, finds minimum spanning tree, and finaly, performs the clustering through dividing the graph into n_clusters (parameter given by the user) by removing n-1 edges with the highest weights.

Required arguments:

• n_clusters - the number of clusters to find

Optional arguments:

• metric - sklearn.metrics.pairwise_distances parameter (default: "euclidean")
• n_jobs - sklearn.metrics.pairwise_distances parameter (default: None)

Example:

import numpy as np
from graph_based_clustering import SpanTreeConnectedComponentsClustering

X = np.array([[0, 1], [1, 0], [1, 1]])

clustering = SpanTreeConnectedComponentsClustering(
    n_clusters=3,
    metric="euclidean",
    n_jobs=-1,
)

clustering.fit(X)
labels_pred = clustering.labels_

# alternative
labels_pred = clustering.fit_predict(X)

Comparing on sklearn toy datasets

ConnectedComponentsClustering

SpanTreeConnectedComponentsClustering

Requirements

Python >= 3.7

Citation

If you use graph-based-clustering in a scientific publication, we would appreciate references to the following BibTex entry:

@misc{dayyass2021graphbasedclustering,
    author       = {El-Ayyass, Dani},
    title        = {Graph-Based Clustering using connected components and spanning trees},
    howpublished = {\url{https://github.com/dayyass/graph-based-clustering}},
    year         = {2021}
}