K Mean Clustering

Prerequisites

Prerequisites for working with K-means clustering are:

Programming language: You need a programming language such as Python or R to implement K-means clustering.
Data manipulation and analysis libraries: Install libraries like NumPy, Pandas, and Scikit-learn (Python) or dplyr, tidyr, and cluster (R) for data manipulation and clustering algorithms.
Integrated Development Environment (IDE): Choose and install a suitable IDE like Jupyter Notebook, Spyder, or RStudio for coding and running your K-means clustering code.
Please note that the specific software requirements may vary based on your chosen programming language and libraries.

Description


K-means clustering is an unsupervised machine learning algorithm used for partitioning data into distinct groups or clusters. It aims to minimize the within-cluster sum of squares, where each data point is assigned to the cluster with the closest mean value. The algorithm iteratively updates the cluster centroids until convergence, resulting in clusters that minimize the distance between data points within each cluster and maximize the distance between different clusters. K-means clustering is widely used for tasks such as customer segmentation, image compression, and anomaly detection. It is relatively simple to understand and implement, making it a popular choice for clustering analysis.

Output


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Summary

K-means clustering is an unsupervised machine learning algorithm that partitions data into distinct clusters based on minimizing the within-cluster sum of squares. It iteratively updates cluster centroids to group data points with similar characteristics. K-means is widely used for tasks like customer segmentation, image compression, and anomaly detection.

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