"Principles of Data Mining" was my first book on the subject, and although I haven't read it all, I can state that this book has done its job in explaining the fundamentals of the topic. It has a very well written recap of statistics and probability and is consistent throughout the chapters in terms of notation, which is important for such texts.

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Principles of Data Mining : Bramer Max: 9788184891669: Books - Amazon.ca

The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve

The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second...

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.

Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

It's what Principles Of Data Mining will give the thoughts for you. To encourage the presence of the Principles Of Data Mining, we support by providing the on-line library. It's actually not for Principles Of Data Mining only; identically this book becomes one collection from many books catalogues.

Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second...

It's what Principles Of Data Mining will give the thoughts for you. To encourage the presence of the Principles Of Data Mining, we support by providing the on-line library. It's actually not for Principles Of Data Mining only; identically this book becomes one collection from many books catalogues.

2001-08-01 The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets?

Principles of Data Mining: Hand, Professor in the Department of Statistics David J, Mannila, Heikki, Smyth, Professor Padhraic: Amazon.nl

Principles of data mining. London, United Kingdom: Springer. This book is designed to be suitable for an introductory course at either undergraduate or masters level. It can be used as a textbook for a taught unit in a degree programme on potentially any of a wide range of subjects including Computer Science, Business Studies, Marketing, Artiﬁcial Intelligence, Bioinformatics and Forensic ...

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.

Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and ...

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.

Principles of Data Mining by David Hand, Heikki Mannila, and Padhraic Smyth provides practioners and students with an introduction to the wide range of algorithms and methodologies in this exciting area. The interdisciplinary nature of the field is matched by these three authors, whose expertise spans statistics, databases, and computer science. The result is a book that not only provides the ...

Principles of Data Mining by David Hand, Heikki Mannila, and Padhraic Smyth provides practioners and students with an introduction to the wide range of algorithms and methodologies in this exciting area. The interdisciplinary nature of the field is matched by these three authors, whose expertise spans statistics, databases, and computer science. The result is a book that not only provides the ...

Principles of Data Mining: Hand, Professor in the Department of Statistics David J, Mannila, Heikki, Smyth, Professor Padhraic: Amazon.nl

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.

Get this from a library! Principles of data mining. [D J Hand; Heikki Mannila; Padhraic Smyth] -- "The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, ...

Principles of Data Mining. 78 pages. Metagenomics: Read Length Matters. 11 pages. Principles of Data Mining. 11 pages. Principles of Data Mining. 5 pages. Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities. 28 pages. Learning Structured Prediction Models. 56 pages. Principles of Data Mining. 4 pages. Visual and ...

Data mining refers to the computational process of automated information extraction from large datasets to facilitate discovery of novel insights. 1 Pattern mining is a fundamental data mining...

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The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

Data mining algorithms are very sensitive to the number of attributes you include. Too many attributes requires extensive CPU and memory resources for processing. Also, not all the attributes are equally important in terms of the prediction accuracy.

Principles of Data Mining Second Edition. Prof. Max Bramer School of Computing University of Portsmouth Portsmouth, UK Series editor Ian Mackie Advisory board Samson Abramsky, University of Oxford, Oxford, UK Karin Breitman, Pontiﬁcal Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil Chris Hankin, Imperial College London, London, UK Dexter Kozen, Cornell University, Ithaca,

The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

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 ...

This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes ...

The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and ...

Principles of Data Mining Max Bramer (auth.) This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a ...

Principles of Data Mining Max Bramer. This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining ...

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.

This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data ...

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 ...

This book constitutes the refereed proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '98, held in Nantes, France, in September 1998. The volume presents 26 revised papers corresponding to the oral presentations given at the conference; also included are refereed papers corresponding to the 30 poster presentations. These papers were

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