Clustering algorithms group data points based on similarity from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Clustering algorithms are a type of unsupervised learning method that aims to group data points together based on their similarity. The goal of these algorithms is to find the inherent structure within the data without any prior knowledge of the groups that the data points belong to. The basic idea behind clustering is to partition the data into groups, or clusters, such that data points within the same cluster are more similar to each other than to those in other clusters.
The process of clustering involves assigning data points to clusters in a way that maximizes the similarity within clusters and minimizes the similarity between clusters. This is done by defining a similarity measure, such as distance, and using it to group data points that are close to each other in the feature space. Clustering algorithms can be broadly categorized into two main types: partitioning algorithms and hierarchical algorithms.
Partitioning algorithms, such as K-means, divide the data into a predetermined number of clusters, with each data point assigned to the cluster that has the nearest centroid. These algorithms iteratively update the cluster assignments until a stopping criterion is met. In contrast, hierarchical algorithms build a tree-like hierarchy of clusters, where each data point starts in its own cluster and clusters are successively merged based on their similarity.
Clustering algorithms are widely used in various fields such as image segmentation, document clustering, and customer segmentation. They can help uncover hidden patterns and structures within the data, which can be useful for tasks such as anomaly detection, recommendation systems, and data compression. By grouping data points based on similarity, clustering algorithms provide a powerful tool for exploring and analyzing complex datasets in a meaningful way.
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