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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

7. Big Data Discretization

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 discretization task transforms continuous numerical data into discrete and bounded values, more understandable for humans and more manageable for a wide range of machine learning methods. With the advent of Big Data, a new wave of large-scale datasets with predominance of continuous features have arrived to industry and academia. However, standard discretizers do not respond well to huge sets of continuous points, and novel distributed discretization solutions are demanded. In this chapter, we review the most relevant contributions to this field in the literature. We begin by enumerating the early proposals on dealing with parallel discretization. Then, we present some distributed solutions capable of scaling on large-scale datasets. We finish with a study of the discretization methods capable of dealing with Big Data streams.

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Vorheriges Kapitel Imperfect Big Data
Nächstes Kapitel Imbalanced Data Preprocessing for Big Data
Fußnoten
1
If the points are in array format, a loop is used to evaluate points, else a distributed map function is used instead.
 
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Metadaten
Titel
Big Data Discretization
verfasst von
Julián Luengo
Diego García-Gil
Sergio Ramírez-Gallego
Salvador García
Francisco Herrera
Copyright-Jahr
2020
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_7

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