Data Mining and Analysis: Fundamental Concepts and Algorithms

This book discusses the fields in the border context of knowledge science and large data approaches. It includes several addition and updates, e.g., inductive mining techniques, the notion of alignment, a considered expanded section on software tools, and a new chapter on process mining during a large. It’s self-contained while at an equivalent time covering the whole process-mining from process discovery to predictive analytics. The decision-making capabilities of research methods can enhance the training and representation of patterns and structures in data. Vice-versa, the characterizations found and modeled by data processing and analysis can improve the efficiency of decision-making algorithms.

After a general introduction to data science and process mining within the business processing necessary to know the rest of the book.

The elemental algorithms in data processing and analysis form the idea for the emerging field of knowledge science, which incorporates automated methods to research patterns and models for all types of knowledge, with applications from scientific discovery to business intelligence and analytics. This textbook provides a broad yet in-depth overview of knowledge mining, integrating related machine learning and statistics concepts. Most parts of the book include investigated data analysis, pattern mining, clustering, and classification. The book lays the essential foundations of those tasks and covers cutting-edge topics like kernel methods, high-dimensional data analysis, and sophisticated graphs and networks. With its algorithmic perspective and wealth of examples, this book offers solid guidance in data processing for college kids, researchers, and practitioners alike. Overall, this book provides a comprehensive overview of the state of the art in process mining.