System for identifying clusters in scatter plots using...

Image analysis – Histogram processing

Reexamination Certificate

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C382S225000

Reexamination Certificate

active

06944338

ABSTRACT:
An apparatus and method for identifying clusters in two-dimensional data by generating a two-dimensional histogram characterized by a grid of bins, determining a density estimate based on the bins, and identifying at least one cluster in the data. A smoothed density estimate is generated using a Gaussian kernel estimator algorithm. Clusters are identified by locating peaks and valleys in the density estimate (e.g., by comparing slope of adjacent bins). Boundaries (e.g., polygons) around clusters are identified using bins after bins are identified as being associated with a cluster. Boundaries can be simplified (e.g., by reducing the number of vertices in a polygon) to facilitate data manipulation.

REFERENCES:
patent: 4207554 (1980-06-01), Resnick et al.
patent: 4599307 (1986-07-01), Saunders et al.
patent: 4661913 (1987-04-01), Wu et al.
patent: 4704891 (1987-11-01), Recktenwald et al.
patent: 4727020 (1988-02-01), Recktenwald
patent: 4745285 (1988-05-01), Recktenwald et al.
patent: 4987086 (1991-01-01), Brosnan et al.
patent: 5018088 (1991-05-01), Higbie
patent: 5314824 (1994-05-01), Schwartz
patent: 5365472 (1994-11-01), Solka et al.
patent: 5445939 (1995-08-01), Anderson
patent: 5465321 (1995-11-01), Smyth
patent: 5528494 (1996-06-01), Moses
patent: 5548661 (1996-08-01), Price et al.
patent: 5572597 (1996-11-01), Chang et al.
patent: 5627040 (1997-05-01), Bierre et al.
patent: 5757954 (1998-05-01), Kuan et al.
patent: 5768413 (1998-06-01), Levin et al.
patent: 5776709 (1998-07-01), Jackson et al.
patent: 5991028 (1999-11-01), Cabib et al.
patent: 6007996 (1999-12-01), McNamara et al.
patent: 6014904 (2000-01-01), Lock
patent: 6115488 (2000-09-01), Rogers et al.
patent: 6148096 (2000-11-01), Pressman et al.
patent: 6226409 (2001-05-01), Cham et al.
patent: 6246972 (2001-06-01), Klimasauskas
patent: 6317517 (2001-11-01), Lu
patent: 6372506 (2002-04-01), Norton
patent: 6620591 (2003-09-01), Dunlay et al.
patent: 6687395 (2004-02-01), Dietz et al.
Haynes, J.L., “Principles of Flow Cytometry”, Cytometry Supplement, 3:7-17 (1988).
Young, Ian T. et al., “Recursive Implementation of the Gaussian Filter”, Signal Processing 44, pp. 139-151 (1995).
Koontz, Warren L.G. et al., “A Nonparametric Valley-Seeking Technique for Cluster Analysis”, IEEE Transactions on Computers, vol. C-21, No. 2, Feb. 1972, pp. 171-178.
Whyatt, J.D. et al., “The Douglas-Peucker Line Simplification Algorithm”, SUC Bulletin vol. 22 No. 1, pp. 17-25 (1988).
Douglas D.H. et al., “Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature”,The Canadian Cartographer10(2):112-122 (1973).
Press, W.H. et al., “Numerical Recipes in Fortran, Second Edition”, Cambridge University Press, pp. 436-448 (1992).
Jones, M.C. et al., “A Brief Survey of Bandwidth Selection for Density Estimation” Journal of the American Statistical Association, vol. 91, No. 433, pp. 401-407 (1996).
Koontz, Warren L.G. et al, “A Graph-Theoretic Approach to Nonparametric Cluster Analysis”, IEEE Transactions on Computers, vol. C-25, No. 9, pp. 936-946, Sep. 1976.

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