Data Mining 101: Core Concepts and Algorithms provides an in-depth exploration of data science and big data methodologies. This book is divided into several chapters, covering a wide range of topics from inductive mining techniques and software tools to the entire process of mining, from discovery to predictive analytics.

We discuss the decision-making capabilities of research methods and how they enhance pattern recognition and data structure representation. In turn, these characterizations improve the efficiency of decision-making algorithms. Starting with a general introduction to data science and process mining, the book builds a solid foundation for understanding key concepts.

Our textbook offers a broad yet detailed overview of data mining, integrating related machine learning and statistical concepts. Topics include data analysis, pattern mining, clustering, classification, kernel methods, high-dimensional data analysis, and complex graphs and networks. Designed for students, researchers, and practitioners, this book provides comprehensive guidance and a wealth of examples.

Data Mining 101: Core Concepts and Algorithms is your essential resource for mastering the art and science of data mining.

Swarnalata Verma

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