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Principles of Data Mining



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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 focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used 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. As an aid to self study, this book 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. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed 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.






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 focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used 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. As an aid to self study, this book 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. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed 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.


This book explains and explores the principal techniques of Data Mining the automatic extraction of implicit and potentially useful information from data which is . by David J Hand Heikki Mannila Professor Padhraic Smyth. This chapter introduces classification one of the most common data mining tasks. Imperial College.


Bramer

The presentation emphasizes intuition rather than rigor. Third European Conference PKDD99 Prague Czech Republic September 1518 1999. The first foundations provides a tutorial overview of the principles underlying data mining algorithms and their application. Principles of Data Mining. More Bramer M. Hand David Heikki Mannila and Padhraic Smyth Principles of Data Mining MIT Press 2001. Article citations. the mathematical principles underlying data mining methods but also provides a valuable perspective on the entire enterprise. Sa yw e are lo oking at the v ariables income and creditca rd sp ending for a data set of N customers at a particular bank. It focuses on. The book consists of three sections. by JM ytkow 1999 Cited by 6 Principles of Data Mining and Knowledge Discovery. A Bradford Book.


E-książki w formacie PDF, epub, mobi Principles of Data Mining PDF. Książki elektroniczne .



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