PUBLICATION • Book chapter
Data Mining and Pattern Recognition
Chapter 17 deals with data mining and pattern recognition, which are methods in data science. A general purpose of data science is pattern discovery from unstructured and heterogeneous sources of data through data mining and machine learning. The chapter discusses data wrangling, clustering analysis, regression trees, neural networks, sentiment analysis and topic models. It goes on to discuss the types of social-ecological systems (SES) problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of data mining and pattern recognition, and suggested further readings on these methods.
Rocha, C.J., and S. Daume. 2021. Data Mining and Pattern Recognition. In: Biggs, R., de Vos, A., Preiser, R., Clements, H., Maciejewski, K. and M. Schlüter (Eds.). The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge, London, UK. Pp. Chapter 17.READ ONLINE