Patent ID: 11960524
Assignee: THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for dynamic cluster-based search and retrieval, the method comprising:
al a server:
retrieving document data for a plurality of documents related to user input:
performing keyword discovery on the document data for determining term related frequency metrics and document related frequency metrics:
representing the plurality of documents as a term-document matrix based on the term related frequency metrics and the document related frequency metrics:
reducing, using latent semantic analysis, the dimensionality of the matrix:
clustering, using a sampling-based k-means clustering algorithm and the dimensionally reduced matrix, the plurality of documents into clusters, wherein the plurality of documents is a predetermined number of most recent documents related to the user input, wherein the most recent documents are determined using document dates in a data store, wherein the sampling-based k-means clustering algorithm executes in constant or near constant time: and sending presentation information to a client device for displaying visual representations of the clusters, wherein each of the visual representations is associated with one or more of the plurality of documents, wherein sending the presentation information includes sending related keywords for the clusters and spatial information for the clusters, and wherein the related keywords for the clusters are determined by:

for each of the clusters:
identifying a centroid of the cluster:
transforming the centroid to a term vector containing term frequency information; and
determining the related keywords based on terms with the highest frequency as indicated by the term vector.