As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more; algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.
Data Mining
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"This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start." - From the foreword by Jim Gray, Microsoft Research "It covers cutting-edge, data mining technology that forward-looking organizations use to successfully tackle problems that are complex, highly dimensional, chaotic, non-stationary (changing over time), or plagued by. The writing style is well-rounded and engaging without subjectivity, hyperbole, or ambiguity. I consider this book a classic already!" - Dr. Tilmann Bruckhaus, StickyMinds.com
About the Author
Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann. Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>
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1 of 1 people found the following review helpful
By pp_fin
Format:Paperback
Books explains basics of machine learnig in a way that quite easy to understand. does not go deep into maths involved, but sufficiently to allow understandinf of algorithms explained.
Very usefull especially if you plan to use weka datamining tool as pretty much everythin available in weka is explained in this book to degree that you choose suitable algorithms and tune them correctly
Very usefull especially if you plan to use weka datamining tool as pretty much everythin available in weka is explained in this book to degree that you choose suitable algorithms and tune them correctly
1 of 2 people found the following review helpful
By Paul Appleby
Format:Paperback
I've tried three times to read this book.
But it is so badly written I can't get through it! It doesn't flow well and it seems to jump into things with little or no context in many places.
At times I even had to refer to other books such as 'Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition' by Jurafsky to get a proper explanation and work through of the theories.
I'll be looking for another book in this area - don't buy this one.
But it is so badly written I can't get through it! It doesn't flow well and it seems to jump into things with little or no context in many places.
At times I even had to refer to other books such as 'Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition' by Jurafsky to get a proper explanation and work through of the theories.
I'll be looking for another book in this area - don't buy this one.
3 of 6 people found the following review helpful
By D. Berry
Format:Paperback
Having read the first edition, the authors earn the extra rating because they've managed to improve on their work and practical WEKA resource offering. Without a doubt, an essential read for people who are both new and experienced in the fields of data mining, descriptive & predictive analytics or state & behavioural modelling.
The volume of material on the market today is still quite limited and in the gap between the first and second edition of this book, quite a lot has actually changed in the field. In my view, book content has only marginally progressed with the times, perhaps in favour of attempting to attract and activate new members, practictioners and commercially oriented researchers to the fore of data mining. It's a bold step to evolve material as the field evolves; those breaking new ground in this area should be more visible and offered greater support.
I believe that there is room in the market now for some revised materials covering anomalised commercial implementations of Advanced Data Mining & AI Concepts. A small community of authors could plug this gap really well.
The volume of material on the market today is still quite limited and in the gap between the first and second edition of this book, quite a lot has actually changed in the field. In my view, book content has only marginally progressed with the times, perhaps in favour of attempting to attract and activate new members, practictioners and commercially oriented researchers to the fore of data mining. It's a bold step to evolve material as the field evolves; those breaking new ground in this area should be more visible and offered greater support.
I believe that there is room in the market now for some revised materials covering anomalised commercial implementations of Advanced Data Mining & AI Concepts. A small community of authors could plug this gap really well.
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