PRINCIPLES OF DATA MINING (UNDERGRADUATE TOPICS IN COMPUTER SCIENCE) BY MAX BRAMER
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PRINCIPLES OF DATA MINING (UNDERGRADUATE TOPICS IN COMPUTER SCIENCE) BY MAX BRAMER PDF
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Review From the reviews of the second edition: “This book introduces the concept of data mining and explains the various techniques involved. … This book is written primarily as a text for a course on data mining. The rich pedagogical features, including illustrations, examples, solved problems, exercises and solutions, a glossary, and references, make it an ideal choice for that purpose. It would be very useful for any reader who wants to gain a good understanding of data mining concepts and techniques.” (Alexis Leon, Computing Reviews, September, 2013) From the Back Cover Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly,
as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
PRINCIPLES OF DATA MINING (UNDERGRADUATE TOPICS IN COMPUTER SCIENCE) BY MAX BRAMER PDF
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PRINCIPLES OF DATA MINING (UNDERGRADUATE TOPICS IN COMPUTER SCIENCE) BY MAX BRAMER PDF
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
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Sales Rank: #876644 in eBooks Published on: 2013-02-26 Released on: 2013-02-26 Format: Kindle eBook
Review From the reviews of the second edition: “This book introduces the concept of data mining and explains the various techniques involved. … This book is written primarily as a text for a course on data mining. The rich pedagogical features, including illustrations, examples, solved problems, exercises and solutions, a glossary, and references, make it an ideal choice for that purpose. It would be very useful for any reader who wants to gain a good understanding of data mining concepts and techniques.” (Alexis Leon, Computing Reviews, September, 2013) From the Back Cover
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Most helpful customer reviews 2 of 2 people found the following review helpful. Perfect By Yamen Batch Very easy to read and follow book for beginners to the subject. I have tried many other texts to get introduced to data mining, all of other texts discuss theories in abstract way without giving helpful examples. This textbook do so, it will explain each and every concept with clear and straightforward examples. Excellent introductory course. I would recommend to those who will start their data mining way. Thanks to the authors, Two thumbs up! 1 of 1 people found the following review helpful. Simple presentation, deep ideas, great book By Amazon Customer The author has somehow written a detailed, insightful book on decision trees without any serious mathematics, using only simple examples. Each algorithm is introduced through a small example. Instead of describing the algorithm using mathematical symbols, he describes the algorithm in words and then shows how to do the computation for the particular example. The result is you get a feel for what the algorithm is doing without any formulae. The examples are carefully chosen to illustrate more subtle details or flaws in the algorithm, so that you can see exactly what he means and understand the underlying ideas. In summary, this is an excellent book for someone beginning in this topic who may not be so confident of their math. Even for someone with a strong math background (in which category I count myself) the examples and explanations are so carefully chosen that the book is a joy to read. 2 of 3 people found the following review helpful. Informative and Easy to Read By EST
Unlike most technical books, this is very well written. Generally it was very clear and the information was presented in an interesting way. I expect his students enjoy the class. I had some trouble with one of the chapters, so I won't say this book is perfect, but it is still one of the best technical books I've seen. See all 4 customer reviews...
PRINCIPLES OF DATA MINING (UNDERGRADUATE TOPICS IN COMPUTER SCIENCE) BY MAX BRAMER PDF
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