Clustering data objects

Details Clustering data objects Clustering data objects

2007-07-26

2011-11-08

Gaffin, Jeffrey A (Department: 2129)

Data processing: artificial intelligence

Machine learning

C706S018000

Reexamination Certificate

active

08055592

ABSTRACT:
A system for clustering data objects includes a module for calculating an importance value of at least one member in a first data object represented as a variable length vector of 0 to N members and a clustering module for dynamically forming a plurality of clusters containing one or more data objects. The clustering module is configured to associate the first data object with at least one of the plurality of clusters in dependence upon the at least one member's similarity value in comparison to members in other data objects. The clustering module may be configured to cluster the first data object into a plurality of clusters if it has at least two members and each member belongs to a different cluster.

REFERENCES:
patent: 6792448 (2004-09-01), Smith
patent: 7000194 (2006-02-01), Newbold
patent: 2001/0018698 (2001-08-01), Uchino et al.
patent: 2002/0188681 (2002-12-01), Gruen et al.
patent: 2003/0131055 (2003-07-01), Yashchin et al.
patent: 1209849 (2007-03-01), None
Friburger et al., N., “Textual Similarity based on Proper Names”, pp. 1-10, 2002.
Lin et al., D., “Induction of Semantic Classes from Natural Language Text”, KDD 01, pp. 317-322, 2001.
Freudenberg et al., J., “A similarity-based method for genome-wide prediction of disease-relevant human genes”, Bioinformatics vol. 18, Suppl, pp. S110-S115, 2002.
Wen et al., J., “Query Clustering Using User Logs”, ACM Transactions on Information Systems, vol. 20, No. 1, pp. 59-81, Jan. 2002.
Resnik, P., “Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language”, Journal of Artificial Intelligence Research 11, pp. 95-130, 1999.
Benkhalifa et al., M., “Integrating WordNet Knowledge to Supplement Training Data in Semi-Supervised Agglomerative Hierarchical Clustering for Text Categorization”, Journal of Intelligent Systems vol. 16, pp. 929-947, 2001.
Torra et al., V., “Exploration of Textual Document Archives Using A Fuzzy Hierarchical Clustering Algorithm in the GAMBAL System”, Information and Processing Management 41, pp. 587-598, Mar. 2004.
Jannink, J. “Thesaurus Entry Extraction from an On-line Dictionary”, Stanford University, pp. 1-9, 1999.
Jin et al., S., “Study of Automatic Abstracting Based on Corpus and Hierarchical Dictionary”, Journal of Software, pp. 308-314, 2000.
Brooks et al., C., “Improved Annotation of the Blogosphere via Autotagging and Hierarchical Clustering”, WWW 2006, pp. 625-631, May 2006.
Li, et al, Real-time topics extraction and visualization in online discussions, Proceedings of 2002 International Conference on Machine Learning and Cybernetics (Cat.No. 02EX583), 2002, pt. 2, p. 928-33 vol. 2 ISBN: 0 7803 7508 4.
Loh, et al., Analyzing Web chat messages for recommending items from a digital library, ICEIS 2004—Proceedings of the Sixth International Conference on Enterprise Information Systems, ICEIS 2004—Proceedings of the Sixth International Conference on Enterprise Information Systems, 2004, p. 41-48, ISBN: 9728865007.
Pimental, et al., Co-text loss in textual chat tools, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), v 2680, Modeling and Using Context, 2003, p. 483-490, ISSN: 0302-9743 CODEN: LNAIE4.

Affiliated with

Also associated with

Rating

Clustering data objects does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Clustering data objects, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clustering data objects will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4301416