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Clustering algorithms group data points based on similarities from "summary" of Machine Learning by Stephen Marsland
Clustering algorithms are used in machine learning to group data points that are similar to each other. These algorithms aim to find patterns in the data that can help organize it into meaningful groups. By identifying similarities between data points, clustering algorithms help to uncover underlying structures within the data. One common type of clustering algorithm is the k-means algorithm, which aims to partition data points into k clusters based on their distance from the mean of each cluster. The algorithm iteratively assigns data points to clusters and updates the cluster centroids until the algorithm converges. By grouping data points based on their proximity to each other, the k-means algorithm can help identify distinct clusters in the data. Another popular clustering algorithm is hiera...
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