Big Data Mining and Complexity

✍ Scribed by Brian C. Castellani, Rajeev Rajaram

Publisher: SAGE Publications
Year: 2022
Tongue: English
Leaves: 233
Series: The SAGE Quantitative Research Kit
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This book offers a much needed critical introduction to data mining and ‘big data’. Supported by multiple case studies and examples, the authors provide:

Digestible overviews of key terms and concepts relevant to using social media data in quantitative research.
A critical review of data mining and ‘big data’ from a complexity science perspective, including its future potential and limitations
A practical exploration of the challenges of putting together and managing a ‘big data’ database
An evaluation of the core mathematical and conceptual frameworks, grounded in a case-based computational modeling perspective, which form the foundations of all data mining techniques

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

✦ Table of Contents

BIG DATA MINING AND COMPLEXITY - FRONT COVER
BIG DATA MINING AND COMPLEXITY
COPYRIGHT
CONTENTS
LIST OF FIGURES
ABOUT THE AUTHORS
CHAPTER 1 - INTRODUCTION
PART I - THINKING CRITICALLY AND COMPLEX
CHAPTER 2 - THE FAILURE OF QUANTITATIVE SOCIAL SCIENCE
CHAPTER 3 - WHAT IS BIG DATA?
CHAPTER 4 - WHAT IS DATA MINING?
CHAPTER 5 - THE COMPLEXITY TURN
PART II - THE TOOLS AND TECHNIQUES OF DATA MINING
CHAPTER 6 - CASE-BASED COMPLEXITY: A DATA MINING VOCABULARY
CHAPTER 7 - CLASSIFICATION AND CLUSTERING
CHAPTER 8 - MACHINE LEARNING
CHAPTER 9 - PREDICTIVE ANALYTICS AND DATA FORECASTING
CHAPTER 10 - LONGITUDINAL ANALYSIS
CHAPTER 11 - GEOSPATIAL MODELLING
CHAPTER 12 - COMPLEX NETWORK ANALYSIS
CHAPTER 13 - TEXTUAL AND VISUAL DATA MINING
CHAPTER 14 - CONCLUSION - ADVANCING A COMPLEX DIGITAL SOCIAL SCIENCE
GLOSSARY
REFERENCES
INDEX

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