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Big Data Analytics Using Local Exceptionality Detection

Big Data Analytics Using Local Exceptionality Detection

Martin Atzmueller, Dennis Mollenhauer, Andreas Schmidt
Copyright: © 2016 |Pages: 18
ISBN13: 9781522502937|ISBN10: 1522502939|EISBN13: 9781522502944
DOI: 10.4018/978-1-5225-0293-7.ch007
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MLA

Atzmueller, Martin, et al. "Big Data Analytics Using Local Exceptionality Detection." Enterprise Big Data Engineering, Analytics, and Management, edited by Martin Atzmueller, et al., IGI Global, 2016, pp. 108-125. https://doi.org/10.4018/978-1-5225-0293-7.ch007

APA

Atzmueller, M., Mollenhauer, D., & Schmidt, A. (2016). Big Data Analytics Using Local Exceptionality Detection. In M. Atzmueller, S. Oussena, & T. Roth-Berghofer (Eds.), Enterprise Big Data Engineering, Analytics, and Management (pp. 108-125). IGI Global. https://doi.org/10.4018/978-1-5225-0293-7.ch007

Chicago

Atzmueller, Martin, Dennis Mollenhauer, and Andreas Schmidt. "Big Data Analytics Using Local Exceptionality Detection." In Enterprise Big Data Engineering, Analytics, and Management, edited by Martin Atzmueller, Samia Oussena, and Thomas Roth-Berghofer, 108-125. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0293-7.ch007

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Abstract

Large-scale data processing is one of the key challenges concerning many application domains, especially considering ubiquitous and big data. In these contexts, subgroup discovery provides both a flexible data analysis and knowledge discovery method. Subgroup discovery and pattern mining are important descriptive data mining tasks. They can be applied, for example, in order to obtain an overview on the relations in the data, for automatic hypotheses generation, and for a number of knowledge discovery applications. This chapter presents the novel SD-MapR algorithmic framework for large-scale local exceptionality detection implemented using subgroup discovery on the Map/Reduce framework. We describe the basic algorithm in detail and provide an experimental evaluation using several real-world datasets. We tackle two algorithmic variants focusing on simple and more complex target concepts, i.e., presenting an implementation of exceptional model mining on large attributed graphs. The results of our evaluation show the scalability of the presented approach for large data sets.

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