aksharas28/Unsupervised-Learning--Clustering-Analysis-on-Fashion-MNIST-Data

To perform cluster analysis on Fashion MNIST dataset using unsupervised learning, K-Means clustering, and Gaussian Mixture Model clustering is used. The main task is to cluster images and identify it as one of many clusters and to perform cluster analysis on fashion MNIST dataset using unsupervised learning. The model’s effectiveness is measured by testing the machine learning scheme on the testing set and the performance can be evaluated by its clustering accuracy. Three tasks performed are K-Means algorithm to cluster original data space of Fashion – MNIST dataset using Sklearns library, an Auto-Encoder based K-Means clustering model is built to cluster the condensed representation of the unlabeled fashion MNIST dataset using Keras and Sklearns library, an Auto-Encoder based Gaussian Mixture Model clustering model is built to cluster

Unsupervised-Learning

Cluster Analysis on Fashion MNIST Dataset using unsupervised learning

Tools Used : Jupyter Notebook, Python Libraries Used: sklearn, K-Means, GMM

To perform cluster analysis on Fashion MNIST dataset using unsupervised learning, K-Means clustering, and Gaussian Mixture Model clustering is used. The main task is to cluster images and identify it as one of many clusters and to perform cluster analysis on fashion MNIST dataset using unsupervised learning. The model’s effectiveness is measured by testing the machine learning scheme on the testing set and the performance can be evaluated by its clustering accuracy. Three tasks performed are K-Means algorithm to cluster original data space of Fashion – MNIST dataset using Sklearns library, an Auto-Encoder based K-Means clustering model is built to cluster the condensed representation of the unlabeled fashion MNIST dataset using Keras and Sklearns library, an Auto-Encoder based Gaussian Mixture Model clustering model is built to cluster