https://github.com/milaan9/clustering_algorithms_from_scratch

Implementing Clustering Algorithms from scratch in MATLAB and Python
https://github.com/milaan9/clustering_algorithms_from_scratch

cluster cluster-analysis clustering-algorithms clustering-analysis clustering-benchmark clustering-methods clustering-models machine-learning-algorithms subspace-clustering tutor-milaan9 unsupervised-learning

Last synced: 5 days ago
JSON representation

Implementing Clustering Algorithms from scratch in MATLAB and Python

• Host: GitHub
• URL: https://github.com/milaan9/clustering_algorithms_from_scratch
• Owner: milaan9
• License: mit
• Created: 2021-07-12T15:41:58.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-09T21:12:50.000Z (about 2 years ago)
• Last Synced: 2024-12-24T12:24:25.822Z (12 days ago)
• Topics: cluster, cluster-analysis, clustering-algorithms, clustering-analysis, clustering-benchmark, clustering-methods, clustering-models, machine-learning-algorithms, subspace-clustering, tutor-milaan9, unsupervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 6.5 MB
• Stars: 198
• Watchers: 4
• Forks: 178
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
 

 

 





 







 



 


 

 

  


# Unsupervised Learning

The Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

# Clustering

Clustering or cluster analysis is an unsupervised learning problem. It is often used as a data analysis technique for discovering interesting patterns in data, such as groups of customers based on their behavior. In clustering problems we split the training examples by unknown characteristics. The algorithm itself decides what characteristic to use for splitting.

Usage examples: market segmentation, social networks analysis, organize computing clusters, astronomical data analysis, image compression, etc.