# clustering_tutorial

**Repository Path**: luminghao/clustering_tutorial

## Basic Information

- **Project Name**: clustering_tutorial
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-21
- **Last Updated**: 2021-06-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Clustering
Notebook of KMeans(++), Gaussian Mixture and Spectral Clustering, with clean implementation.

I hope they could be helpful for you to study the ideas of the 3 clustering algorithms. Any suggestion, email me: jekyll4168_at_icloud_dot_com .

1. [Intuition with scikit-learn](0_scikit-learn_KMeans_GMM_and_Spectral_Clustering.ipynb)
2. [KMeans and KMeans++](1_KMeans_and_KMeans++.ipynb)
3. [Gaussian Mixture](2_Gaussian_Mixture_and_EM.ipynb)
4. [Spectral Clustering](3_Spectral_Clustering.ipynb)

# Note
I didn't refactor the functions in these notebooks into OOP style. So if you want a clean class implementation, you can find in my contributions to [MLAlgorithms](https://github.com/rushter/MLAlgorithms/).
* [Gaussian Mixture](https://github.com/rushter/MLAlgorithms/blob/master/mla/gaussian_mixture.py)

# Dependency
Python 3 with **numpy, matplotlib and sklearn**.

Though I didn't test carefully, it should run well with Python 2 too.

# LICENSE
MIT