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Data Mining

Data Mining

Martin Atzmueller
Copyright: © 2012 |Pages: 20
ISBN13: 9781609607418|ISBN10: 1609607414|EISBN13: 9781609607425
DOI: 10.4018/978-1-60960-741-8.ch005
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MLA

Atzmueller, Martin. "Data Mining." Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, IGI Global, 2012, pp. 75-94. https://doi.org/10.4018/978-1-60960-741-8.ch005

APA

Atzmueller, M. (2012). Data Mining. In P. McCarthy & C. Boonthum-Denecke (Eds.), Applied Natural Language Processing: Identification, Investigation and Resolution (pp. 75-94). IGI Global. https://doi.org/10.4018/978-1-60960-741-8.ch005

Chicago

Atzmueller, Martin. "Data Mining." In Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, 75-94. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-741-8.ch005

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Abstract

Data Mining provides approaches for the identification and discovery of non-trivial patterns and models hidden in large collections of data. In the applied natural language processing domain, data mining usually requires preprocessed data that has been extracted from textual documents. Additionally, this data is often integrated with other data sources. This chapter provides an overview on data mining focusing on approaches for pattern mining, cluster analysis, and predictive model construction. For those, we discuss exemplary techniques that are especially useful in the applied natural language processing context. Additionally, we describe how the presented data mining approaches are connected to text mining, text classification, and clustering, and discuss interesting problems and future research directions.

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