Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)

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This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) Review

This book covers many methods in data mining and machine learning. The best thing to me is that it tells each story from a theoretical way, but not a superficial way. It really helps you understand these machine learning methods from a deep perspective. Reading this book did let me think more thoroughly.
Of course the good thing can be a bad thing in that, if you do not have enough background in statistics and math, this book can be very difficult to read. The famous Hastie, Tibshirani and Friedman's book is a good one and someone may complain that that book is not easy to read unless you have solid background in math. However Clarke's book, to me, is even harder.
If you really want to learn the details in data mining, this book would be an ideal resource.

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