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, Second Edition (Undergraduate Topics in Computer Science)

Bramer, Max

Springer

Assuming no programming experience on the reader's part, this book starts from scratch and aims to arrive at the point where quite powerful programs can be written in the Prolog language. This second edition is revised to be fully compatible with SWI-Prolog.

Logic Programming with Prolog

Computers & Technology

Programming

This book explains the principal techniques of data mining, for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed examples, with a focus on algorithms rather than mathematical formalism.

Principles of Data Mining (Undergraduate Topics in Computer Science)

Computer Science

Springer-Nature New York Inc

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, it 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.\nEach chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. 
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 shows how it can be used for classification.\nFrom the Back Cover\nThis 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, it 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.\nEach chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. 
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 shows how it can be used for classification.

Introductory textbook covering all the main features of the 'web programming' languages PHP and MySQL together with detailed examples that will enable readers (whether students on a taught course or independent learners) to use them to create their own applications or understand existing ones. A particular focus is the use of PHP to generate MySQL commands from a script as it is executed. Each chapter includes aims, a summary and practical exercises (with solutions) to support learning. Chapters are designed to stand alone as far as possible, so that they can be studied independently of the rest of the text by those with some previous knowledge of the languages. There is a comprehensive glossary of technical terms, together with extensive appendices for quick reference of language features.

Web Programming with PHP and MySQL: A Practical Guide

Artificial Intelligence (AI) is a rapidly growing inter-disciplinary field with a long and distinguished history that involves many countries and considerably pre-dates the development of computers. It can be traced back at least as far as Ancient Greece and has evolved over time to become a major subfield of computer science in general. This state-of-the-art survey not only serves as a "position paper" on the field from the viewpoint of expert members of the IFIP Technical Committee 12, its Working Groups and their colleagues, but also presents overviews of current work in different countries. The chapters describe important relatively new or emerging areas of work in which the authors are personally involved, including text and hypertext categorization; autonomous systems; affective intelligence; AI in electronic healthcare systems; artifact-mediated society and social intelligence design; multilingual knowledge management; agents, intelligence and tools; intelligent user profiling; and supply chain business intelligence. They provide an interesting international perspective on where this significant field is going at the end of the first decade of the twenty-first century.

Artificial Intelligence. An International Perspective: An International Perspective (Lecture Notes in Computer Science, 5640)

Principles of Data Mining

Springer India

See the best price to sell, buy, or rent books by Bramer, Max. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Bramer, Max

See the best price to sell, buy, or rent books by Bramer, Max. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Principles of Data Mining, Second Edition (Undergraduate Topics in Computer Science)

Logic Programming with Prolog

Principles of Data Mining (Undergraduate Topics in Computer Science)

Principles of Data Mining (Undergraduate Topics in Computer Science)

Web Programming with PHP and MySQL: A Practical Guide

Artificial Intelligence. An International Perspective: An International Perspective (Lecture Notes in Computer Science, 5640)

Principles of Data Mining