https://github.com/peteprattis/user-clusters-and-k-means-fold-for-classifier-evaluation

A Matlab script that applies the basic sequential clustering to evaluate the number of user groups by using the hierarchical clustering and k-means algorithms. Using the k-means fold the classifiers that are a neural network and the other least squares to evaluate them.
https://github.com/peteprattis/user-clusters-and-k-means-fold-for-classifier-evaluation

classifier clusters computer-science hierarchical-clustering k-fold k-means-clustering matlab program sequential-clustering student

Last synced: about 1 month ago
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A Matlab script that applies the basic sequential clustering to evaluate the number of user groups by using the hierarchical clustering and k-means algorithms. Using the k-means fold the classifiers that are a neural network and the other least squares to evaluate them.

• Host: GitHub
• URL: https://github.com/peteprattis/user-clusters-and-k-means-fold-for-classifier-evaluation
• Owner: PetePrattis
• License: mit
• Created: 2019-11-12T17:48:29.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-12T18:03:00.000Z (over 5 years ago)
• Last Synced: 2024-11-17T11:53:16.319Z (3 months ago)
• Topics: classifier, clusters, computer-science, hierarchical-clustering, k-fold, k-means-clustering, matlab, program, sequential-clustering, student
• Language: MATLAB
• Size: 9.77 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# A Matlab Exercise / Project

**This is a Matlab project from my early days as a Computer Science student**

_This programm was created for the fifth semester class Pattern Recognision 

and it is the final project necessary to pass the class_

> #### Description of project

>

>A Matlab script that applies the basic sequential clustering to evaluate the number of user groups by using the hierarchical clustering and k-means algorithms. Using the k-means fold the classifiers that are a neural network and the other least squares to evaluate them.

>

> #### Implementation of project

>

> 1. Apply the basic sequential schema to estimate the number of user groups according to their preferences.

> 2. Based on the estimation of Step 1, apply the k-means algorithm and the hierarchical clustering algorithm.

> 3. Using the 5-fold format provided, design, implement, and evaluate two classifiers, which solve the following problem: if a user and a movie is given, the classifier decides whether the user saw the movie (class 1) or not (class 2) . One classifier will be a neural network and the other a least squares.

> #### About this project

>

> - The MovieLens dataset used for 100k ratings https://grouplens.org/datasets/movielens/100k/

> - This program was written in Matlab IDE

> - This repository was created to show the variety of the work I did and experience I gained as a student

>