Big Data Mining and Complexity

In addition to being a Professor of Sociology at Durham University, I am currently Adjunct Professor of Psychiatry (Northeast Ohio Medical University), Fellow of the Wolfson Research Institute for Health and Wellbeing, and Co-Editor of the Routledge Complexity in Social Science series. I am also a member of the editorial board for International Journal of Social Research Methodology and Complexity, Governance and Networks. Trained as a sociologist, clinical psychologist and methodologist (statistics and computational social science), I have spent the past ten years developing a new case-based, data-mining approach to modeling complex social systems - called the SACS Toolkit - which my colleagues and I have used to help researchers, policy makers and service providers address and improve complex public health issues such as community health and well-being; infrastructure and grid reliability; mental health and inequality; big data and data mining; and globalization and global civil society. We have also recently developed the COMPLEX-IT R-studio software app, which allows everyday users seamless access to such high-powered techniques as machine intelligence, neural nets, and agent-based modeling to make better sense of the complex world(s) in which they live and work. Rajeev Rajaram is a Professor of Mathematics at Kent State University. Rajeev's primary training is in control theory of partial differential equations and he is currently interested in applications of differential equations and ideas from statistical mechanics and thermodynamics to model and measure complexity. He and Brian Castellani have worked together to create a new case - based method for modeling complex systems, called the SACS Toolkit, which has been used to study topics in health, health care, societal infrastructures, power - grid reliability, restaurant mobility, and depression trajectories. More recently, he is interested in mathematical properties of entropy based diversity measures for probability distributions.

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