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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.
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.
