Machine Learning notebooks for refreshing concepts.

Implementing Clustering Algorithms from scratch in MATLAB and Python

A simple python implementation of Fuzzy C-means algorithm.

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

A data discovery and manipulation toolset for unstructured data

Huge-scale, high-performance flow cytometry clustering in Julia

An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods

Fast OPTICS clustering in Cython + gradient cluster extraction

PlotTwist - a web app for plotting and annotating time-series data

Feature extraction from GEOJson nuclei and tissue segmentation maps

genome sized sequences clustering

Interactive HTML canvas based implementation of k-means.

An R Package for Bayesian Nonparametric Clustering. We plan to implement several models.

MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems

A D-Stream clustering algorithm implementation in Python

Sentence Clustering and visualization. Created Date: 25 Apr 2018

Coupled clustering of single cell genomic data

C++ implementation of a MCMC sampler for the (canonical) SBM

Density-Based Clustering Validation

GPU accelerated K-Means and Mean Shift clustering in Tensorflow.

Code for paper "InfoShield: Generalizable Information-Theoretic Human-Trafficking Detection" (ICDE 2021)

Novel joint clustering method with scRNA-seq and CITE-seq data

Jupyter notebook based multiplex image processing pipeline.

A Java program to cluster a dataset in CSV format using k-means clustering

A Data Mining Framework for Air Route Clustering

This repository contains a roadmap with examples for machine learning, providing a step-by-step guide to help you navigate the field and acquire the necessary knowledge and skills

Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Non-Negative Matrix Factorization for Gene Expression Clustering

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors, and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

A package to understand and analyze complex networks and more in general complex data. It is a collection of clustering techniques inspired by social science and communication theories.