Chapter 1. Introduction -- Chapter 2. Data Analysis -- Chapter 3. Fuzzy Cognitive Maps -- Chapter 4. Data Modeling -- Chapter 5. Network analysis, accuracy and stability of the job-satisfaction structures -- Chapter 6. The proposed data-driven glassoFCM method -- Chapter 7. Thesis Conclusions.

Fuzzy cognitive maps (FCMs) have gained popularity in the scientific community due to their capabilities in modeling and decision making for complex problems. This book presents a novel algorithm called glassoFCM to enable automatic learning of FCM models from data. Specifically, glassoFCM is a combination of two methods, glasso (a technique originated from machine learning) for data modeling and FCM simulation for decision making. The book outlines that glassoFCM elaborates simple, accurate, and more stable models that are easy to interpret and offer meaningful decisions. The research results presented are based on an investigation related to a real-world business intelligence problem to evaluate characteristics that influence employee work readiness. Finally, this book provides readers with a step-by-step guide of the 'fcm' package to execute and visualize their policies and decisions through the FCM simulation process.