linkedcluster

LinkedCluster is a concept and, in some implementations, a software approach for organizing data into clusters that are interconnected by links representing relationships between clusters. This stands in contrast to traditional partitioning methods, which assign each data point to a single cluster without explicit cross-cluster connections.

Construction typically starts with standard clustering to form base clusters, using algorithms such as k-means, DBSCAN, or spectral clustering. A subsequent step builds a cluster graph where each node represents a cluster and edges reflect significant inter-cluster relationships. Edges may be weighted by measures of similarity, transition probability, or co-membership strength, and can be directed or undirected. This results in a connected graph that conveys both within-cluster cohesion and between-cluster connectivity.

Variants of the approach include graph-based clustering with cluster-centric nodes, hierarchical or multi-resolution representations that link clusters across levels, and dynamic or streaming scenarios where links are updated over time to reflect evolving data.

Applications span exploratory data analysis, social network analysis, topic modeling and text clustering, genomics and other bioinformatics contexts, anomaly detection, and recommender systems. The linkedcluster representation can reveal pathways, flows, or dependencies that are not apparent in flat clusterings and can support interactive visualization and navigation of complex datasets.

Advantages include a richer depiction of inter-cluster structure and improved interpretability of relationships. Limitations involve greater modeling complexity, sensitivity to linkage thresholds, and potential for dense graphs that challenge interpretation. See also graph clustering, spectral clustering, community detection, and cluster graphs.