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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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书目名称Data Science and Big Data: An Environment of Computational Intelligence
编辑Witold Pedrycz,Shyi-Ming Chen
视频video
概述Discusses implementations and case studies.Identifies the best design practices.Assesses data analytics business models and practices in industry, health care, administration and business.Includes sup
丛书名称Studies in Big Data
图书封面Titlebook: Data Science and Big Data: An Environment of Computational Intelligence;  Witold Pedrycz,Shyi-Ming Chen Book 2017 Springer International Pu
描述This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business..Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy..Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address da
出版日期Book 2017
关键词Big Data; Data Science; Computational Intelligence; Data Analytics; Internet of Things
版次1
doihttps://doi.org/10.1007/978-3-319-53474-9
isbn_softcover978-3-319-85162-4
isbn_ebook978-3-319-53474-9Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Developing Modified Classifier for Big Data Paradigm: An Approach Through Bio-Inspired Soft Computind on supervised features following conventional data mining principle. However, the classification of majority or positive class is over-sampled by taking each minority class sample. Definitely, significant computationally intelligent methodologies have been introduced. Following the philosophy of d
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Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing oachieve effective selection of data pre-processing techniques towards effective selection of relevant attributes, sampling of representative training and test data, and appropriate dealing with missing values and noise. More importantly, this framework allows the employment of suitable machine learn
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Event Detection in Location-Based Social Networksause of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of
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Big Data for Effective Management of Smart Gridsata, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data. There are several techniques, based on Big Data ana
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