Event Mining for Explanatory ModelingBy: Laleh Jalali
Table of Contents
This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data. Such a model may be used as the basis for predictions and corrective actions. The idea is to create, via an iterative process, a model that explains causal relationships in the form of structural and temporal patterns in the data. The first phase is the data-driven process of hypothesis formation, requiring the analysis of large amounts of data to find strong candidate hypotheses. The second phase is hypothesis testing, wherein a domain expert’s knowledge and judgment is used to test and modify the candidate hypotheses.
The book will be useful for both practitioners and researchers working in different computer science fields. Data miners/scientists and data analysts can benefit from high-performance event mining techniques introduced in this book. Also, The book is accessible to many readers and not necessarily just those with strong backgrounds in computer science. Public health professionals, epidemiologists, physicians, and social scientists can benefit from the new perspective of this book in harnessing the value of heterogeneous big data for building diverse real-life applications.