clusters

A cluster is a group of objects that are more similar to each other than to objects in other groups. In many fields, clusters are defined by proximity in a feature space or by shared properties, and the boundaries between clusters are often imperfect or probabilistic.

In data analysis, cluster analysis is an unsupervised learning task that aims to partition a set of objects into subsets such that objects within the same subset are more alike. Common methods include k-means, hierarchical clustering, DBSCAN, and Gaussian mixture models. The choice of algorithm, distance metric, and feature representation influence the shape and size of clusters, which can be spherical, elongated, or of arbitrary form. Validation often relies on internal measures or domain knowledge, rather than external labels.

In computing, a cluster refers to a group of connected computer systems that work together to achieve a common goal. Clusters provide scalability, high availability, and fault tolerance for tasks such as scientific simulations, large-scale data processing, and web services. Cluster management software coordinates resource allocation, monitoring, and failover among nodes.

In economics and geography, industry or economic clusters describe geographic concentrations of related firms, suppliers, service providers, and associated institutions. Clusters can accelerate innovation through knowledge spillovers, labor pooling, and specialized infrastructure, though they may also raise competition and housing pressures. Examples exist in many regions around the world.

In astronomy, a star cluster is a group of stars bound by gravity, such as open clusters and globular clusters. In biology and genomics, clustering techniques organize genes or expression profiles into co-expressed groups for analysis. Across domains, clustering remains a foundational concept for revealing structure and organization in complex data and systems.