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data mining

Overview page. Subjects: Science and Mathematics.

The processing of large amounts of data in order to extract new kinds of useful information from it, based on patterns and relationships. See also informatics.

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Data Mining

Carolin Strobl.

in The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2

March 2013; p ublished online October 2013 .

Article. Subjects: Psychology; Research Methods in Psychology. 13672 words.

The term data mining refers to a variety of exploratory data analysis techniques developed in computer sciences and computational statistics. This chapter points out the commonalities and...

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data mining

Nigar Hashimzade, Gareth Myles and John Black.

in A Dictionary of Economics

January 2017; p ublished online January 2017 .

Reference Entry. Subjects: Economics. 70 words.

Extraction of useful information from large data sets. Data mining is not the same as data analysis: data analysis is focused on estimation and assessment of an existing model, while data...

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data mining

Alisdair Rogers, Noel Castree and Rob Kitchin.

in A Dictionary of Human Geography

January 2013; p ublished online September 2013 .

Reference Entry. Subjects: Human Geography. 45 words.

A computational method for analysing large quantities of quantitative *data in order to discover and extract features within the data that warrant further attention. Data mining is...

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data mining

Edited by John Black, Nigar Hashimzade and Gareth Myles.

in A Dictionary of Economics

January 2012; p ublished online May 2013 .

Reference Entry. Subjects: Economics. 70 words.

Extraction of useful information from large datasets. This is construed in a broader sense than data analysis. While data analysis is focused on estimation and assessment of the existing...

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data mining

Jonathan Law.

in A Dictionary of Finance and Banking

March 2018; p ublished online March 2018 .

Reference Entry. Subjects: Banking; Social Sciences; Economics. 48 words.

The process of extracting useful knowledge from the huge volumes of data kept in modern computer databases. Sophisticated algorithms and statistical techniques are used to identify...

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data mining

Edited by Jonathan Law.

in A Dictionary of Business and Management

January 2016; p ublished online February 2016 .

Reference Entry. Subjects: Business and Management. 80 words.

The process of extracting useful knowledge from the huge volumes of data kept in modern computer databases. Sophisticated algorithms and statistical techniques are used to identify...

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data mining

Jonathan Law.

in A Dictionary of Business and Management

January 2009; p ublished online January 2009 .

Reference Entry. Subjects: Business and Management. 64 words.

The process of extracting useful knowledge from the huge volumes of data kept in modern computer databases. Sophisticated algorithms and statistical techniques are used to identify...

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data mining

Charles Doyle.

in A Dictionary of Marketing

January 2016; p ublished online May 2016 .

Reference Entry. Subjects: Marketing. 76 words.

The process of gathering or locating pertinent information from a database. This is particularly key to customer marketing as it enables multiple combinations, for example customers who...

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Data Mining

Jacob Furst, Daniela Stan Raicu and Leonard A. Jason.

in Handbook of Methodological Approaches to Community-Based Research

December 2015; p ublished online January 2016 .

Chapter. Subjects: Clinical Psychology. 6759 words.

Data mining (also known as artificial intelligence) can uncover patterns and relationships within large samples of people, organizations, or communities that would not otherwise be evident...

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spatial data mining

Alisdair Rogers, Noel Castree and Rob Kitchin.

in A Dictionary of Human Geography

January 2013; p ublished online September 2013 .

Reference Entry. Subjects: Human Geography. 73 words.

The *data mining of spatial databases to identify interesting and useful spatial patterns. Given the size and complexity of many spatial databases, data mining methods apply techniques such...

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Text Data Mining

Marti A. Hearst.

in The Oxford Handbook of Computational Linguistics

January 2005; p ublished online September 2012 .

Article. Subjects: Computational Linguistics. 4690 words.

Text expresses a vast range of information, but encodes this information in a form that is difficult to decipher automatically. This article defines data mining, information retrieval, and...

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Clinical Data-Mining

Irwin Epstein.

November 2009; p ublished online May 2010 .

Book. 240 pages.

Clinical data-mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making, and...

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Data Mining as Global Governance

Fleur Johns.

in The Oxford Handbook of Law, Regulation and Technology

July 2017; p ublished online January 2017 .

Article. Subjects: IT and Communications Law; Law and Society. 10350 words.

Data mining technologies are increasingly prominent in development and aid initiatives in which context they may be understood to be doing work of global governance. This chapter explains...

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Automated Text Processing and Data Mining

Maurizio Borghi and Stavroula Karapapa.

in Copyright and Mass Digitization

March 2013; p ublished online May 2013 .

Chapter. Subjects: Intellectual Property Law. 13653 words.

One of the most prominent features of mass digitization is the automated processing of works for various research-related and commercial purposes. This includes text mining or linguistic...

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Microarray data analysis and mining approaches

Francesca Cordero, Marco Botta and Raffaele A. Calogero.

in Briefings in Functional Genomics

December 2007; p ublished online January 2008 .

Journal Article. Subjects: Genetics and Genomics. 10202 words.

Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale...

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Data mining in bioinformatics using Weka

Eibe Frank, Mark Hall, Len Trigg, Geoffrey Holmes and Ian H. Witten.

in Bioinformatics

October 2004; p ublished online April 2004 .

Journal Article. Subjects: Bioinformatics and Computational Biology. 0 words.

Summary: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection—common data...

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Data mining with iPlant

Lisa Martin, Charis Cook, Naim Matasci, Jason Williams and Ruth Bastow.

in Journal of Experimental Botany

January 2015; p ublished online October 2014 .

Journal Article. Subjects: Plant Sciences and Forestry. 4065 words.

High-throughput sequencing technologies have rapidly moved from large international sequencing centres to individual laboratory benchtops. These changes have driven the ‘data deluge’ of...

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Statistical data mining procedures in generalized cox regressions

Zhen Wei.

in The Oxford Handbook of Credit Derivatives

January 2011; p ublished online September 2012 .

Article. Subjects: Economics; Financial Markets; Econometric and Statistical Methods and Methodology: General. 15716 words.

This article, which introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining...

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BAGEL2: mining for bacteriocins in genomic data

Anne de Jong, Auke J. van Heel, Jan Kok and Oscar P. Kuipers.

in Nucleic Acids Research

July 2010; p ublished online May 2010 .

Journal Article. Subjects: Chemistry; Biochemistry; Bioinformatics and Computational Biology; Genetics and Genomics; Molecular and Cell Biology. 2625 words.

Mining bacterial genomes for bacteriocins is a challenging task due to the substantial structure and sequence diversity, and generally small sizes, of these antimicrobial peptides. Major...

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Bayesian rule learning for biomedical data mining

Vanathi Gopalakrishnan, Jonathan L. Lustgarten, Shyam Visweswaran and Gregory F. Cooper.

in Bioinformatics

March 2010; p ublished online January 2010 .

Journal Article. Subjects: Bioinformatics and Computational Biology. 6106 words.

Motivation: Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially lead to the discovery...

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