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K-Means

K-means is an unsupervised clustering algorithm widely used in data analysis to partition unlabelled data into distinct groups, known as clusters. The algorithm operates by identifying K centroids, which represent the center of each cluster, and assigning data points to the nearest centroid based on distance. This process iteratively refines the positions of the centroids and the assignments of data points until convergence is achieved. K-means is popular for its simplicity and efficiency, making it applicable in various fields such as market segmentation, image processing, and pattern recognition. Understanding K-means is essential for effective data clustering and analysis.

K-means: A Complete Introduction

Towards Data Science

K-means is an unsupervised clustering algorithm designed to partition unlabelled data into a certain number (thats the “ K”) of distinct groupings. In other words, k-means finds observations that…

K-Means Explained

Towards Data Science

This article will outline a conceptual understanding of the k-Means algorithm and its associated python implementation using the sklearn library. K means is a clustering algorithm with many use cases…...

“K-means Clustering” in 200 words.

Analytics Vidhya

K-means is a machine learning algorithm designed to find “clusters” in data by measuring the “distance” between points. This is typically done through the “elbow method” by plotting the “error” of…

K-Means tricks for fun and profit

Towards Data Science

K-Means is an elegant algorithm. It’s easy to understand (make random points, move them iteratively to become centers of some existing clusters) and works well in practice. When I first learned about…...

K Means without libraries — Python

Towards Data Science

Kmeans is a widely used clustering tool for analyzing and classifying data. Often times, however, I suspect, it is not fully understood what is happening under the hood. This isn’t necessarily a bad…

K-means Clustering in a Nutshell

Towards AI

K-means is an unsupervised clustering machine learning model. In Unsupervised Learning, the data set does not contain a target value to train the data. Clustering is a technique in which we group…

K-Means Practical

Towards Data Science

Unsupervised learning is often looked on as a little ‘unconventional’ in the data science world, especially when empirically provable results are desired. K-Means clustering enjoys some enduring…

K-means from scratch with NumPy

Towards Data Science

K-means is the simplest clustering algorithm out there. It’s easy to understand and to implement, making it a great starting point when trying to understand the world of unsupervised learning…

K-Means Clustering From Scratch

Towards Data Science

K-means clustering (referred to as just k-means in this article) is a popular unsupervised machine learning algorithm (unsupervised means that no target variable, a.k.a. Y variable, is required to…

K Means Clustering with Python

Towards Data Science

K Means Clustering is an unsupervised machine learning algorithm. It takes in mixed data and divides the data into small groups/clusters based on the patterns in the data. In order to explain the…

K-means Clustering

Analytics Vidhya

K-means Clustering is an unsupervised machine learning technique. It aims to partition n observations into k clusters. As we have seen in other Machine learning Algorithms, we have a loss function…

ACTIVITY — K Means on Wholesale Customers

Python in Plain English

This is part of a series on: How K-Means Work Part 1 of K Means by Dr. Alvin Ang dr-alvin-ang.medium.com https://github.com/DRALVINANG/Machine-Learning-with-Python-Training/blob/main/K%20Means/K_Means...