Principles and Theory for Data Mining and Machine Learning

1. Front Matter

   Pages i-xiv

   PDF

2. Variability, Information, and Prediction

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 1-52

3. Local Smoothers

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 53-116

4. Spline Smoothing

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 117-170

5. New Wave Nonparametrics

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 171-230

6. Supervised Learning: Partition Methods

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 231-306

7. Alternative Nonparametrics

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 307-363

8. Computational Comparisons

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 365-404

9. Unsupervised Learning: Clustering

   • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

   Pages 405-491

10. Learning in High Dimensions

    • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

    Pages 493-568

11. Variable Selection

    • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

    Pages 569-678

12. Multiple Testing

    • Bertrand Clarke, Ernest Fokoué, Hao Helen Zhang

    Pages 679-742

13. Back Matter

    Pages 1-38

    PDF

The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with ongoing appreciation.

• Clustering
• classification
• data mining
• linear regression
• machine learning
• supervised learning
• unsupervised learning

From the reviews:

“PhD level students, and researchers and practitioners in statistical learning and machine learning. … text assumes a thorough training in undergraduate statistics and mathematics. Computed examples that include R code are scattered through the text. There are numerous exercises, many with commentary that sets out guidelines for exploration. … The over-riding reason for staying with the independent, symmetric unimodal error model is surely that no one book can cover everything! Within these bounds, this book gives a careful treatment that is encyclopedic in its scope.” (John H. Maindonald, International Statistical Review, Vol. 79 (1), 2011)

“It is an appropriate textbook for a PhD level course and can also be used as a reference or for independent reading. … an excellent resource for researchers and students interested in DMML. … the authors have done an outstanding job of covering important topics and providing relevant statistical theory and computational resources. I can see myself teaching a statistical learning class using this book and comfortably recommend it to any researcher with a solid mathematical background who wants to be engaged in this field.” (Jeongyoun Ahn, Journal of the American Statistical Association, Vol. 106 (493), March, 2011)

“This book provides an encyclopedic monograph on this field from a statistical point of view. … A salient feature of this book is its coverage of theoretical aspects of DMML techniques. … Additionally, plenty of exercises and computational examples with R codes are provided to help one brush up on the technical content of the text.” (Kazuho Watanabe, Mathematical Reviews, Issue 2012 i)

• Dept. Statistics, University of British Columbia, Vancouver, Canada

  Bertrand Clarke

• Dept. Science & Mathematics, Kettering University, Flint, U.S.A.

  Ernest Fokoue

• Dept. Statistics, North Carolina State University, Raleigh, U.S.A.

  Hao Helen Zhang

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