Titlebook: Data Science and Big Data: An Environment of Computational Intelligence; Witold Pedrycz,Shyi-Ming Chen Book 2017 Springer International Pu

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978-3-319-85162-4Springer International Publishing AG 2017

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Osmud Rahman,Benjamin C. M. Fungossible: (i) adapt available algorithms or design new approaches such that they can run on a distributed computing environment (ii) develop model-based learning techniques that can be trained efficiently on a small subset of the data and make reliable predictions. In this chapter two recent algorith

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Svetlana Karelskaia,Ekaterina Zugas, the membership assignments, and distance or similarity functions. In this chapter we describe different data types, membership functions, and similarity functions and discuss the pros and cons of using each of them. Conventional similarity functions evaluate objects in the vector space. Contraril

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Osmud Rahman,Benjamin C. M. Fungbeyond the capability of commonly used software tools to capture, curate and manage within a tolerable elapsed time and also beyond the processing feasibility of the single machine architecture. In addition to the traditional structured data, the new avenue of NoSQL Big Data has urged a call to expe

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Andreas Vogel,Oktay Ergünay,Müfiz Alpmenearning as a powerful tool of big data processing. In machine learning context, learning algorithms are typically evaluated in terms of accuracy, efficiency, interpretability and stability. These four dimensions can be strongly related to veracity, volume, variety and variability and are impacted by

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N. Bayülke,E. Inan,A. Dogan,A. Hürataevents in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the Whi

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