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Computational and statistical methods for analysing big data with applications

By: Liu, Shen.
Contributor(s): McGree, James | Ge, Zongyuan | Xie, Yang.
Publisher: Amsterdam Academic Press 2016Description: viii, 194 p.ISBN: 9780128037324.Subject(s): Big data | Data mining - Statistical methods | Mathematics - AppliedDDC classification: 005.74015 Summary: Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. (http://store.elsevier.com/product.jsp?isbn=9780128037324&_requestid=263240)
List(s) this item appears in: Big data | VR_Data Analytics, Data Visualization and Big Data | Big Data_Book Display
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Table of Contents:

1. Introduction

Abstract
1.1 What is big data?
1.2 What is this book about?
1.3 Who is the intended readership?
References

2. Classification methods

Abstract
2.1 Fundamentals of classification
2.2 Popular classifiers for analysing big data
2.3 Summary
References

3. Finding groups in data

Abstract
3.1 Principal component analysis
3.2 Factor analysis
3.3 Cluster analysis
3.4 Fuzzy clustering
Appendix
References

4. Computer vision in big data applications

Abstract
4.1 Big datasets for computer vision
4.2 Machine learning in computer vision
4.3 State-of-the-art methodology: deep learning
4.4 Convolutional neural networks
4.5 A tutorial: training a CNN by ImageNet
4.6 Big data challenge: ILSVRC
4.7 Concluding remarks: a comparison between human brains and computers
Acknowledgements
References

5. A computational method for analysing large spatial datasets

Abstract
5.1 Introduction to spatial statistics
5.2 The HOS method
5.3 MATLAB functions for the implementation of the HOS method
5.4 A case study
References

6. Big data and design of experiments

Abstract
6.1 Introduction
6.2 Overview of experimental design
6.3 Mortgage Default Example
6.4 U.S.A domestic Flight Performance – Airline Example
6.5 Conclusion
References

7. Big data in healthcare applications

Abstract
7.1 Big data in healthcare-related fields
7.2 Predicting days in hospital (DIH) using health insurance claims: a case study
Acknowledgement
References

8. Big data from mobile devices

Abstract
8.1 Data from wearable devices for health monitoring
8.2 Mobile devices in transportation

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.

Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.


(http://store.elsevier.com/product.jsp?isbn=9780128037324&_requestid=263240)

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