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Functional magnetic resonance imaging / Neuroimaging / Spatial data analysis / Geostatistics / Spatial analysis / Resampling / G factor / K-d tree / Statistics / Magnetic resonance imaging / Cognitive science


Detecting Significant Multidimensional Spatial Clusters Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213
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Document Date: 2004-10-29 17:05:32

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File Size: 115,45 KB

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City

Boston / /

Company

ACM-SIGMOD Intl / 3M / 10th ACM SIGKDD Intl / /

Country

United States / /

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Facility

Computer Science Carnegie Mellon University / Northwestern University / /

IndustryTerm

real-world applications / then base-case-search / data mining applications / brain imaging datasets / brain imaging / spatial data mining problems / base-case-search / retail sales / spatial data mining / epidemiological applications / binary search trees / brain imaging data / computational algorithms / search procedure / data mining / multiresolution search method / multiresolution search procedure / multidimensional multiresolution algorithm / overlap-search / /

Organization

Emergency Department / Northwestern University / Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh / Carnegie Mellon University / /

Person

Daniel B. Neill / Andrew W. Moore / Francisco Pereira / /

ProvinceOrState

South Carolina / /

PublishedMedium

Machine Learning / /

Region

Western Pennsylvania / /

Technology

data mining / multidimensional multiresolution algorithm / machine learning / /

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