A Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.

The source code for our work "Towards better Validity: Dispersion based Clustering for unsupervised Person Re-identification"

An Interactive Approach to Understanding Unsupervised Learning Algorithms

Graph Agglomerative Clustering Library

Customer Personality Analysis Using Clustering

Agglomerative hierarchical clustering in JavaScript

Build Agglomerative hierarchical clustering algorithm from scratch, i.e. WITHOUT any advance libraries such as Numpy, Pandas, Scikit-learn, etc.

A machine learning clustering model for customer segmentation to define marketing strategy.

PCA. Clustering Algorithms. Business Analytics.

Customer Segmentation Using Unsupervised Machine Learning Algorithms

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

Clustering Analysis Performed on the Customers of a Mall based on some common attributes such as salary, buying habits, age and purchasing power etc, using Machine Learning Algorithms.

Supervised hierarchical clustering

Clustering and recognition of faces in a photo album

This project shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation and DBSCAN.

Linkage Methods for Hierarchical Clustering

Clustering Algorithms based on centroids namely K-Means Clustering, Agglomerative Clustering and Density Based Spatial Clustering

Agglomerative based clustering on gene expression dataset

This repository contains a collection of fundamental topics and techniques in machine learning. It aims to provide a comprehensive understanding of various aspects of machine learning through simplified notebooks. Each topic is covered in a separate notebook, allowing for easy exploration and learning.