Principles of Data Mining pdf

David J. Hand is a prominent British statistician born on June 30, 1950, in Peterborough, United Kingdom. He holds the position of Professor of Statistics at Imperial College, London. Over the years, he has established himself as a leading figure in the field of statistics and data analysis.

Academic and Professional Achievements:

• Emeritus Professor of Mathematics and Senior Research Investigator at Imperial College, London.
• Formerly held the Chair in Statistics at Imperial College, London.
• Fellow of the British Academy and an Honorary Fellow of the Institute of Actuaries.
• Served as President of the Royal Statistical Society on two occasions.
• Served as a non-executive director of the UK Statistics Authority for eight years.
• Member of the European Statistical Advisory Committee.
• Chairs the National Statistician's Expert User Advisory Committee and the ONS's Centre for Applied Data Ethics Advisory Committee.
• Previous Chair of the Research Board of Imperial College's Data Science Institute and Chair of the UK's Administrative Data Research Network.
• Chief Scientific Advisor to Winton Capital Management for eight years.

Authorship:

David J. Hand is a prolific author with a notable body of work, including:

• Over 300 scientific papers.
• 32 books covering various aspects of data analysis and statistics.
• His recent books include "Amy's Luck," "Dark Data," "From GDP to Sustainable Wellbeing," "Measurement: A Very Short Introduction," and "Information Generation: How Data Rules Our World."

Awards and Recognitions:

• Awarded the Guy Medal of the Royal Statistical Society in 2002.
• In 2012, he and his research group received the Credit Collections and Risk Award for Contributions to the Credit Industry.
• Honored with the George Box Medal in 2016.
• Received the Research Medal of the International Federation of Classification Societies in 2019.
• In 2013, he was appointed OBE (Officer of the Order of the British Empire) for his outstanding contributions to research and innovation.

David J. Hand's remarkable career and contributions have left an indelible mark on the field of statistics, making him a respected authority and prolific author in the realm of data analysis and statistical science.

Principles of Data Mining pdf by David Hand

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.