Better Data Mining and Image Processing Better Data Mining and Image Processing Better Data Mining and Image Processing Better Data Mining and Image Processing Better Data Mining and Image Processing Better Data Mining and Image Processing by Clustering of Large Databases by Clustering of Large Databases by Clustering of Large Databases by Clustering of Large Databases by Clustering of Large Databases by Clustering of Large Databases

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8 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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WARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197US
Better Data Mining and Image ProcessingBetter Data Mining and Image ProcessingBetter Data Mining and Image ProcessingBetter Data Mining and Image ProcessingBetter Data Mining and Image ProcessingBetter Data Mining and Image Processing
by Clustering of Large Databasesby Clustering of Large Databasesby Clustering of Large Databasesby Clustering of Large Databasesby Clustering of Large Databasesby Clustering of Large Databases
INVENTORS • Tian Zhang, Raghu Ramakrishnan, Miron Livny
Since its founding in 1925 as the patenting and
licensing organization for the University of Wisconsin-
Madison, WARF has been working with business and
industry to transform university research into products
that benefit society. WARF intellectual property
managers and licensing staff members are leaders in
the field of university-based technology transfer. They
are familiar with the intricacies of patenting, have
worked with researchers in relevant disciplines,
understand industries and markets, and have
negotiated innovative licensing strategies to meet the
individual needs of business clients.
The Wisconsin Alumni Research Foundation (WARF) is seeking commercial
partners interested in developing a system for processing massive data sets,
like image signals, with normal memory capacity and speed.
OVERVIEW
As more digital information is collected and ‘warehoused,’ analyzing it to extract patterns
is highly useful to a breadth of fields, from consumer research to genetics and satellite
imaging.
One such ‘data mining’ method is clustering. In clustering, sparse and crowded areas of
data space are identified, allowing overall distribution patterns to be discovered.
Clustering provides better understanding of patterns and improves how data is organized
and retrieved. When amounts of data are very large, however, existing algorithms are
unsuitable or time-consuming.
THE INVENTION
UW–Madison researchers have developed a statistics and distance-based clustering
technique for processing very large databases with conventional computer memory and
operating speeds.
The process is organized using a clustering feature tree structure, with each feature
comprising the number, the linear and the square sums of the data points in the cluster. A
dense region of data points is treated collectively, whereas points in sparsely occupied
regions can be taken as outliers and removed. Clustering is carried out continuously as
data are received and processed by restructuring the tree to accommodate new
information.
APPLICATIONS
• Data mining
• Image filtering from satellites, television cameras, medical scanners etc.
KEY BENEFITS
Wisconsin Alumni Research Foundation    |    614 Walnut Street, 13th Floor    |    Madison, WI 53726    |    licensing@warf.org    |    www.warf.org
• Handles massive data sets
• Processes faster
• Functions with conventional computers
• No exhaustive scanning of all data points
• Maximizes accuracy and efficiency
• Users can tune performance by controlling several parameters.
ADDITIONAL INFORMATION
Intellectual Property Status
View U.S. Patent No. 5,832,182 in PDF format.
Tech Fields
Information Technology - Computing methods
Information Technology - Software
CONTACT INFORMATION
For current licensing status, please contact our team at
licensing@warf.org
or 608.263.2500.
WARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197USWARF: P95197US
Wisconsin Alumni Research Foundation    |    614 Walnut Street, 13th Floor    |    Madison, WI 53726    |    licensing@warf.org    |    www.warf.org