System and method for detecting generalized space-time clusters

Data processing: structural design – modeling – simulation – and em – Simulating nonelectrical device or system

Reexamination Certificate

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C706S020000

Reexamination Certificate

active

10777548

ABSTRACT:
A system for detecting clusters in space and time using input data on occurrences of a phenomenon and characteristics at a plurality of locations and times comprises an expectation generation module determining expected occurrences of a phenomena, and an occurrence modeling module determining actual occurrences of the phenomena. The system further comprises a search module searching the expected occurrences and the actual occurrences for a plurality of candidate solutions, wherein each solution is represented as a set of points in the three-dimensional space, and wherein each point corresponds to a location at a time. The system comprises a convex container module determining at least one solution corresponding to a selected convex container shape from the plurality of candidate solutions, and a solution evaluation module determining a strength metric for each solution determined by the convex container module, the search module selecting a dominant cluster in the input data.

REFERENCES:
patent: 6229918 (2001-05-01), Toyama
Editors: Joseph Glaz and N. Balakrishnan; Title: Scan Statistic and Applications; Date: 1999; Publisher: Birkhäuser; ISBN: 978-0-8176-4041-5; pp. 303-319.
Authors: M Kulldorff, N Nagarwalla; Title: Spatial Disease Clusters: Detection and Inference; Date: Apr. 30, 1995; Publisher: John Wiley & Sons, Ltd.; Journal: Statistics in Medicine; vol. 14; pp. 799-810.
Authors: M Kulldorff, W F Athas, E J Feurer, B A Miller, and C R Key; Title: Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico; Date: Sep. 1998; Journal: American Journal of Public Health; vol. 88, No. 9; pp. 1377-1380.
Rakesh Agrawal, Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications, Proceedings of the ACM-SIGMOD, International Conference on Management of Data, 1998.
Martin Kulldorff, A Spatial Scan Statistic pp. 1481-1497. Commun. Statist.-Theory Meth., 26(6), (1997).

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