Principles of Data Mining pdf

大卫·汉德 是一位著名的英国统计学家，1950 年 6 月 30 日出生于英国彼得伯勒。 他担任伦敦帝国理工学院统计学教授。 多年来，他已成为统计和数据分析领域的领军人物。

学术和专业成就：

• 伦敦帝国理工学院数学名誉教授和高级研究员。
• 曾任伦敦帝国理工学院统计学系主任。
• 英国科学院院士和精算师协会荣誉院士。
• 曾两次担任英国皇家统计学会主席。
• 担任英国统计局非执行董事八年。
• 欧洲统计咨询委员会成员。
• 担任国家统计学家专家用户咨询委员会和国家统计局应用数据伦理中心咨询委员会主席。
• 曾任帝国理工学院数据科学研究所研究委员会主席和英国行政数据研究网络主席。
• 担任元盛资本管理公司首席科学顾问八年。

作者身份：

大卫·汉德 是一位多产作家，拥有大量著名作品，包括：

• 超过 300 篇科学论文。
• 32本书涵盖数据分析和统计的各个方面。
• 他最近的著作包括《艾米的运气》、《暗数据》、《从 GDP 到可持续福祉》、《测量：非常简短的介绍》和《信息生成：数据如何统治我们的世界》。

奖项和认可：

• 2002年荣获英国皇家统计学会盖伊奖章。
• 2012年，他和他的研究小组因对信贷行业的贡献而获得了信贷催收和风险奖。
• 2016年荣获乔治博克斯奖章。
• 2019年荣获国际船级社联合会研究奖章。
• 2013年，他因其在研究和创新方面的杰出贡献而被任命为OBE（大英帝国勋章军官）。

大卫·汉德 卓越的职业生涯和贡献在统计学领域留下了不可磨灭的印记，使他成为数据分析和统计科学领域受人尊敬的权威和多产的作家。

Principles of Data Mining pdf 大卫·汉德

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.