Clustering

A prominent method within unsupervised machine learning is clustering, which aims to uncover patterns within unlabeled datasets by grouping similar data objects based on their inherent characteristics. Assessing the similarity between diverse data points, such as genetic information and consumer profiles, involves employing metrics like Euclidean distance. The arrangement of clusters is fine-tuned based on a stopping criterion, achieved through iterative assignments of these points to clusters using algorithms like DBSCAN or K-Means. The final stage of this process involves interpreting the clusters to discern underlying data patterns. This versatile technique is widely applicable across numerous domains, including its use in targeted marketing for consumer segmentation, image processing to classify images, and biological data analysis to uncover gene expression patterns. These applications enable the discovery of concealed correlations and insights within datasets.