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SEARCHING FOR LEUKAEMIA CLUSTERS USING A GEOGRAPHICAL ANALYSIS MACHINE
Author(s) -
Openshaw Stan,
Charlton Martin,
Craft Alan
Publication year - 1988
Publication title -
papers in regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.937
H-Index - 64
eISSN - 1435-5957
pISSN - 1056-8190
DOI - 10.1111/j.1435-5597.1988.tb01117.x
Subject(s) - computer science , exploratory analysis , exploratory data analysis , task (project management) , data mining , machine learning , data science , artificial intelligence , engineering , systems engineering
The paper describes a new approach to the graphical analysis of cancer data. A Geographical Analysis Machine is built which simultaneously solves the problems of post hot: hypothesis testing, handling data errors, and the task of generating and evaluating hypotheses in an exploratory environment. The methodology is demonstrated by reanalyzing cancer data for Northern England in such a way that for the first time the complex universe of all geographical hypotheses is enumerated, evaluated, and all the significant cancer clusters displayed.