Clustering is an unsupervised machine learning technique that involves grouping similar data points together based on their characteristics. The primary goal of clustering is to identify patterns and structures within unlabeled data, allowing for exploratory data analysis. Various algorithms, such as k-means, DBSCAN, and hierarchical clustering, can be employed to achieve this, each with its own approach to defining and finding clusters ¹⁵.

In clustering, data points within a cluster are more similar to each other than to those in other clusters. The similarity is often measured using distance metrics, such as Euclidean distance or Manhattan distance. Since there is no ground truth to validate the clusters, evaluating the results is crucial, and it may require iterating through different algorithms or parameters to achieve satisfactory outcomes ³⁵.

Clustering has numerous applications, including customer segmentation, document organization, and dimensionality reduction. By grouping similar entities, clustering helps in profiling attributes and gaining insights into underlying patterns within the data ²⁵.