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K Means Clustering

K-means clustering is an unsupervised machine learning algorithm used to partition data into K distinct, non-overlapping subsets or clusters. The algorithm works by assigning data points to the nearest cluster centroid and then updating the centroids based on the mean of the points in each cluster. This process is repeated until the centroids stabilize, meaning that the assignments of data points to clusters no longer change significantly.

One of the key aspects of K-means is the initialization of centroids. A poor initialization can lead to suboptimal clustering results. By default, the algorithm runs multiple times with different centroid seeds (n_init set to 10), which helps in finding a better solution. However, if n_init is set to only 1, the algorithm may yield less reliable results due to the lack of diversity in initial centroid positions ¹.

K-means clustering has various real-world applications, including customer segmentation, document classification, house price estimation, and fraud detection ³. It is particularly useful in scenarios where the data is large and complex, allowing for efficient data analysis and pattern recognition ⁵.

K-means Clustering

Scikit-learn Examples

K-means Clustering The plots display firstly what a K-means algorithm would yield using three clusters. It is then shown what the effect of a bad initialization is on the classification process: By se...

K-Means Clustering

Level Up Coding

Today we are going to go through a quick example of K-Means clustering on a 2-Dimensional set of data. Below is the link to the ipynb if you want to follow along. In the first two cells we import our…...

K-Means Clustering

Towards Data Science

Customer Segmentation, Document Classification, House Price Estimation, and Fraud Detection. These are just some of the real world applications of clustering. There are many other use cases for this…

k- Means Clustering

Analytics Vidhya

It divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels this means it’s an unsupervised algorithm…

K-Means Data Clustering

Towards Data Science

In today’s world with the increased usage of Internet, the amount of data generated is incomprehensively massive. Even if the nature of individual data is simple, the sheer volume of data to be…

K-Means Clustering Algorithm

Analytics Vidhya

Brief: K-means clustering is an unsupervised learning method. In this post, I introduce the idea of unsupervised learning and why it is useful. Then I talk about K-means clustering: mathematical…

K-Means Clustering — An Introduction

Towards Data Science

K-means clustering is a popular unsupervised algorithm that groups data into ‘k’ number of clusters, where k is defined by the user.

Clustering With K-Means

Kaggle Learn Courses

Introduction This lesson and the next make use of what are known as *unsupervised learning* algorithms. Unsupervised algorithms don't make use of a target; instead, their purpose is to learn some pro...

Clustering with K-means

Towards Data Science

One of the most commonly used techniques of unsupervised learning is clustering. As the name suggests, clustering is the act of grouping data that shares similar characteristics. In machine learning…

All About K-Means Clustering

Towards AI

In this article we will understand a clustering algorithm by answering the following question: 1. What is clustering? 2. What are the real-world applications of clustering? 3. How does K-means Cluster...

K-Means Clustering Algorithm

Level Up Coding

Brief: K-means clustering is an unsupervised learning method. In this post, I introduce the idea of unsupervised learning and why it is useful. Then I talk about K-means clustering: mathematical…

K-Means Clustering - Overview

Learn Data Science

K-Means Clustering - Overview How do I discover natural groupings or segments in my data? Introduction Often we are given a large mass of data with no training labels. That is, the data does not tell ...