Data Mining With Decision Trees: Theory And Applications (2nd Edition)
Data Mining With Decision Trees: Theory And Applications (2nd Edition)

Author: Oded Z Maimon

Publisher: World Scientific

Published: 2014-09-03

Total Pages: 328

ISBN-13: 9814590096

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Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:

Data Mining with Decision Trees
Data Mining with Decision Trees

Author: Lior Rokach

Publisher: World Scientific

Published: 2008

Total Pages: 263

ISBN-13: 9812771719

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This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Data Mining With Decision Trees: Theory And Applications
Data Mining With Decision Trees: Theory And Applications

Author: Lior Rokach

Publisher: World Scientific

Published: 2007-12-17

Total Pages: 263

ISBN-13: 9814474185

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This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:

Data Mining with Decision Trees
Data Mining with Decision Trees

Author: Lior Rokach

Publisher: World Scientific Publishing Company Incorporated

Published: 2014-09

Total Pages: 305

ISBN-13: 9789814590075

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Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Scales well to big data Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many open source data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Data Mining
Data Mining

Author: Mehmed Kantardzic

Publisher: John Wiley & Sons

Published: 2019-11-12

Total Pages: 672

ISBN-13: 1119516048

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Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Data Mining
Data Mining

Author: Ian H. Witten

Publisher: Elsevier

Published: 2011-02-03

Total Pages: 665

ISBN-13: 0080890369

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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Wavelet Theory Approach To Pattern Recognition (2nd Edition)
Wavelet Theory Approach To Pattern Recognition (2nd Edition)

Author: Yuan Yan Tang

Publisher: World Scientific

Published: 2009-07-06

Total Pages: 482

ISBN-13: 9814467715

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The 2nd edition is an update of the book Wavelet Theory and its Application to Pattern Recognition published in 2000. Three new chapters, which are research results conducted during 2001-2008, are added. The book consists of three parts — the first presents a brief survey of the status of pattern recognition with wavelet theory; the second contains the basic theory of wavelet analysis; the third includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition.

Data Science and Machine Learning for Non-Programmers
Data Science and Machine Learning for Non-Programmers

Author: Dothang Truong

Publisher: CRC Press

Published: 2024-02-23

Total Pages: 768

ISBN-13: 1003835635

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As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

Fundamentals of Supervised Machine Learning
Fundamentals of Supervised Machine Learning

Author: Giovanni Cerulli

Publisher: Springer Nature

Published: 2023-11-14

Total Pages: 416

ISBN-13: 3031413377

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This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms. After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online. The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work.

Applications of Bioinformatics in Rice Research
Applications of Bioinformatics in Rice Research

Author: Manoj Kumar Gupta

Publisher: Springer Nature

Published: 2021-09-24

Total Pages: 363

ISBN-13: 9811639973

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This book summarizes the advanced computational methods for mapping high-density linkages and quantitative trait loci in the rice genome. It also discusses the tools for analyzing metabolomics, identifying complex polyploidy genomes, and decoding the extrachromosomal genome in rice. Further, the book highlights the application of CRISPR-Cas technology and methods for understanding the evolutionary development and the de novo evolution of genes in rice. Lastly, it discusses the role of artificial intelligence and machine learning in rice research and computational tools to analyze plant-pathogen co-evolution in rice crops.