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2020 | OriginalPaper | Buchkapitel

5. Data Reduction for Big Data

verfasst von : Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera

Erschienen in: Big Data Preprocessing

Verlag: Springer International Publishing

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Abstract

Data reduction in data mining selects/generates the most representative instances in the input data in order to reduce the original complex instance space and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of this chapter is to provide several ideas, algorithms, and techniques to deal with the data reduction problem on Big Data. We begin by analyzing the first ideas on scalable data reduction in single-machine environments. Then we present a distributed data reduction method that solves many of the scalability problems derived from the sequential approaches. Next we provide a case of use of data reduction algorithms in Big Data. Lastly, we study a recent development on data reduction for high-speed streaming systems.

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Titel: Data Reduction for Big Data
verfasst von: Julián Luengo
Diego García-Gil
Sergio Ramírez-Gallego
Salvador García
Francisco Herrera
Verlag: Springer International Publishing
Buch: Big Data Preprocessing
Print ISBN: 978-3-030-39104-1

Electronic ISBN: 978-3-030-39105-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39105-8_5

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