Principles of Data Mining

The burgeoning interest in data mining is driven by a common challenge spanning various fields: how to effectively manage, access, model, and ultimately comprehend vast datasets? Traditionally, different aspects of data mining were tackled separately by disparate disciplines. However, "Principles of Data Mining" breaks new ground as the first truly interdisciplinary text on the subject, amalgamating insights from information science, computer science, and statistics.

A Threefold Approach:

The book unfolds across three key sections, each contributing to a comprehensive understanding of data mining:

• Foundations: In this initial section, readers receive a tutorial-style overview of the fundamental principles that underpin data mining algorithms and their practical applications. The emphasis here is on fostering intuition rather than delving into excessive rigor.
• Data Mining Algorithms: This section is dedicated to the construction of algorithms designed to tackle specific problems in a systematic and principled manner. The covered algorithms encompass a wide spectrum, including classification and regression trees, association rules, belief networks, classical statistical models, nonlinear models like neural networks, and local "memory-based" models.
• Real-World Applications: The final section culminates in an exploration of how the previously covered analyses seamlessly integrate when confronted with real-world data mining challenges. Topics span the crucial role of metadata, strategies for handling missing data, and the significance of data preprocessing.

A Bridge Between Disciplines:

Principles of Data Mining by David J. Hand transcends disciplinary boundaries, serving as a pioneering work that bridges information science, computer science, and statistics. It equips readers with a holistic perspective on data mining, emphasizing not only the "how" but also the "why" behind its methodologies.

In a world awash with data, this book stands as an essential resource for anyone seeking to harness the power of data mining to unlock insights, patterns, and knowledge from large and complex datasets. David J. Hand's interdisciplinary approach ensures that this book remains a valuable asset for both novices and experts in the field.