Data Mining: An Introduction

KEY FEATURES The book covers a wide range of topics related to data mining, including deep learning, decision tree induction, association rule mining, analytical statistics techniques, clustering, and more. The book provides numerous case studies and examples that illustrate how data mining techniques can be applied in real-world settings, such as in banking, education, software engineering, and weather forecasting. The book emphasizes the importance of machine learning in data mining and provides a detailed discussion of various machine learning techniques, such as regression and classification. The book uses Python programming language and MapReduce algorithms to illustrate various data mining techniques, making it accessible to readers with programming experience. The book also covers the concept of fuzzy data mining, which involves dealing with uncertain and imprecise data. It is relevant to a wide range of readers, including students, researchers, and professionals working in data-driven fields.