https://github.com/gbroques/k-means

K-Means and Bisecting K-Means clustering algorithms implemented in Python 3.
https://github.com/gbroques/k-means

bisecting-kmeans centroid clustering euclidean-distance k-means k-means-clustering manhattan-distance python-3-6 python3

Last synced: about 1 month ago
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K-Means and Bisecting K-Means clustering algorithms implemented in Python 3.

• Host: GitHub
• URL: https://github.com/gbroques/k-means
• Owner: gbroques
• License: mit
• Created: 2018-04-15T19:32:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T00:58:04.000Z (about 2 years ago)
• Last Synced: 2023-03-01T09:56:26.976Z (almost 2 years ago)
• Topics: bisecting-kmeans, centroid, clustering, euclidean-distance, k-means, k-means-clustering, manhattan-distance, python-3-6, python3
• Language: Jupyter Notebook
• Size: 134 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# K-Means Clustering

[![Build Status](https://travis-ci.org/gbroques/k-means.svg?branch=master)](https://travis-ci.org/gbroques/k-means)

[![Coverage Status](https://coveralls.io/repos/github/gbroques/k-means/badge.svg?branch=master)](https://coveralls.io/github/gbroques/k-means?branch=master)

A k-means clustering implementation in Python.

API inspired by Scikit-learn.

**Reference:**  *Introduction to Data Mining* (1st Edition) by Pang-Ning Tan

Section 8.2, Page 496

## Usage

```python

from typing import List

from dataviz import generate_clusters

from dataviz import plot_clusters

from kmeans import KMeans

def generate_data(num_clusters: int, seed=None) -> List[List]:

    num_points = 20

    spread = 7

    bounds = (1, 100)

    return generate_clusters(num_clusters, num_points, spread, bounds, bounds, seed)

num_clusters = 4

clusters = generate_data(num_clusters, seed=1)

k_means = KMeans(num_clusters=num_clusters, seed=4235)

k_means.fit(clusters)

plot_clusters(clusters, k_means.labels_, k_means.centroids_)

```

![png](k-means-plot.png)

```python

print('Total Sum of Squared Error (SSE): {}'.format(k_means.inertia_))

```

    Total Sum of Squared Error (SSE): 230.0880894560679