Document clustering that applies a locality sensitive hashing function to a feature vector to obtain a limited set of candidate clusters
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United States of America Patent
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Sep 14, 2010
Grant Date -
Aug 28, 2008
app pub date -
Feb 25, 2008
filing date -
Feb 26, 2007
priority date (Note) -
Expired
status (Latency Note)
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Abstract
Documents from a data stream are clustered by first generating a feature vector for each document. A set of cluster centroids (e.g., feature vectors of their corresponding clusters) are retrieved from a memory based on the feature vector of the document using a locality sensitive hashing function. The centroids may be retrieved by retrieving a set of cluster identifiers from a cluster table, the cluster identifiers each indicative of a respective cluster centroid, and retrieving the cluster centroids corresponding to the retrieved cluster identifiers from a memory. Documents may then be clustered into one or more of the candidate clusters using distance measures from the feature vector of the document to the cluster centroids.
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