Abstract: A clustering method yields a searchable hierarchy to speed retrieval, and can function dynamically with a changing document population. Nodes of the hierarchy climb up and down the emerging hierarchy based on locally sensed information. Like previous ant clustering algorithms, the inventive process is dynamic, decentralized, and anytime. Unlike them, it yields a hierarchical structure. For simplicity, and reflecting our initial application in the domain of textual information, the items being clustered are documents, but the principles may be applied to any collection of data items.

Abstract: A clustering method yields a searchable hierarchy to speed retrieval, and can function dynamically with a changing document population. Nodes of the hierarchy climb up and down the emerging hierarchy based on locally sensed information. Like previous ant clustering algorithms, the inventive process is dynamic, decentralized, and anytime. Unlike them, it yields a hierarchical structure. For simplicity, and reflecting our initial application in the domain of textual information, the items being clustered are documents, but the principles may be applied to any collection of data items.

Abstract: A clustering method yields a searchable hierarchy to speed retrieval, and can function dynamically with a changing document population. Nodes of the hierarchy climb up and down the emerging hierarchy based on locally sensed information. Like previous ant clustering algorithms, the inventive process is dynamic, decentralized, and anytime. Unlike them, it yields a hierarchical structure. For simplicity, and reflecting our initial application in the domain of textual information, the items being clustered are documents, but the principles may be applied to any collection of data items.