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

In this Repo a simple Sklearn Model will be trained and pushed to MLFlow

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

Learn how to responsibly deliver value with ML.

A simple and lightweight genetic algorithm for optimization of any machine learning model

Forecast dynamically at scale with this unique package. pip install scalecast

Simple and flexible ML workflow engine.

PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors.

Python package for stacking (machine learning technique)

A modular active learning framework for Python

Tools for diffing and merging of Jupyter notebooks.

Nixtla is an open-source time series forecasting library.

Python Machine Learning Jupyter Notebooks (ML website)

Price forecasting of SGB and IRFC Bonds and comparing there returns

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

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

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows.

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching.