Siemens Corporation

Siemens Aktiengesellschaft

Bernhard Glomann

Claus Neubauer

Fabian Moerchen

Klaus Brinker

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.

Document clustering that applies a locality sensitive hashing function to a feature vector to obtain a limited set of candidate clusters

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 and a relative age of each of the cluster centroids. 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. A list of cluster identifiers in the cluster table may be maintained based on the relative age of cluster centroids corresponding to the cluster identifiers. Cluster identifiers that correspond to cluster centroids with a relative age exceeding a predetermined threshold are periodically removed from the list of cluster identifiers.

Online document clustering using TFIDF and predefined time windows

An object-oriented system for managing relational numerical data includes a relation interface that provides access to a relation and its attributes and tuples, where an attribute is a data item with a numeric value and a relation is a set of n-tuples of attribute values, where the relation interface maintains its tuples in a fixed order and is adapted to selecting subsets of attributes and/or tuples where a new relation interface instance is created, an iterator interface for iterating over the tuples of a relation, where the iterator is adapted to acquiring the tuple attribute data as data is being accessed, and a notification interface for notifying other classes whenever changes occur in a relation interface instance.

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Bayonne, NJ, United States of America

Bernhard Glomann