K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

Last update: Nov 01, 2021

Overview

K Means Algorithm

What is K Means

This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of predefined non- overlapping distinct clusters or subgroups. It makes the data points of inter clusters as similar as possible and also tries to keep the clusters as far as possible. It allocates the data points to a cluster if the sum of the squared distance between the cluster’s centroid and the data points is at a minimum, where the cluster’s centroid is the arithmetic mean of the data points that are in the cluster. A less variation in the cluster results in similar or homogeneous data points within the cluster.

Sources :

How K Means works

Specify number of clusters K.
Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.
Compute the euclidean distance
Assign each data point to the closest cluster (centroid).
Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster.

K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

Related tags

Overview

K Means Algorithm

What is K Means

Sources :

How K Means works

Flow Chart

K Means in action

2D:

3D:

Owner

A Tools that help Data Scientists and ML engineers train and deploy ML models.

A logistic regression model for health insurance purchasing prediction

Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

The MLOps is the process of continuous integration and continuous delivery of Machine Learning artifacts as a software product, keeping it inside a loop of Design, Model Development and Operations.

End to End toy example of MLOps

This is a curated list of medical data for machine learning

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Diabetes Prediction with Logistic Regression

(3D): LeGO-LOAM, LIO-SAM, and LVI-SAM installation and application

Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

Python package for machine learning for healthcare using a OMOP common data model

This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform.

A Python implementation of GRAIL, a generic framework to learn compact time series representations.

A concept I came up which ditches the idea of "layers" in a neural network.

Customers Segmentation with RFM Scores and K-means

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

AP1 Transcription Factor Binding Site Prediction

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

Gaussian Process Optimization using GPy