Literature DB >> 11084711

Data mining: data analysis on a grand scale?

P Smyth1.   

Abstract

Modern data mining has evolved largely as a result of efforts by computer scientists to address the needs of 'data owners' in extracting useful information from massive observational data sets. Because of this historical context, data mining to date has largely focused on computational and algorithmic issues rather than the more traditional statistical aspects of data analysis. This paper provides a brief review of the origins of data mining as well as discussing some of the primary themes in current research in data mining, including scalable algorithms for massive data sets, discovering novel patterns in data, and analysis of text, web, and related multimedia data sets.

Mesh:

Year:  2000        PMID: 11084711     DOI: 10.1177/096228020000900402

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  5 in total

1.  CiteSpace II: visualization and knowledge discovery in bibliographic databases.

Authors:  Marie B Synnestvedt; Chaomei Chen; John H Holmes
Journal:  AMIA Annu Symp Proc       Date:  2005

2.  Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.

Authors:  Andrei Rodin; Thomas H Mosley; Andrew G Clark; Charles F Sing; Eric Boerwinkle
Journal:  J Comput Biol       Date:  2005       Impact factor: 1.479

Review 3.  Imaging phenotypes and genotypes in schizophrenia.

Authors:  Jessica A Turner; Padhraic Smyth; Fabio Macciardi; James H Fallon; James L Kennedy; Steven G Potkin
Journal:  Neuroinformatics       Date:  2006

4.  Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis.

Authors:  Suk-Tae Seo; In-Hee Lee; Chang-Sik Son; Hee-Joon Park; Hyoung-Seob Park; Hyuck-Jun Yoon; Yoon-Nyun Kim
Journal:  Healthc Inform Res       Date:  2010-12-31

5.  Detection of independent associations in a large epidemiologic dataset: a comparison of random forests, boosted regression trees, conventional and penalized logistic regression for identifying independent factors associated with H1N1pdm influenza infections.

Authors:  Yohann Mansiaux; Fabrice Carrat
Journal:  BMC Med Res Methodol       Date:  2014-08-26       Impact factor: 4.615

  5 in total

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