Big data mining and complexity
Author(s)
Bibliographic Information
Big data mining and complexity
（The SAGE quantitative research kit / editors, Malcolm Williams, Richard D. Wiggins, D. Betsy McCoach）
SAGE, c2021
- : [pbk.]
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Note
Includes bibliographical references (p. [195]-206) and index
Description and Table of Contents
Description
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
Chapter 1: Introduction
Part 1: Thinking Complex and Critically
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 2: 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 Modeling
Chapter 12: Complex Network Analysis
Chapter 13: Textual and Visual Data Mining
Chapter 14: Conclusion: Advancing A Complex Digital Social Science
by "Nielsen BookData"