Java Data Mining
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Data Mining
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Author : Ian H. Witten
language : en
Publisher: Elsevier
Release Date : 2011-02-03
Data Mining written by Ian H. Witten and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-03 with Computers categories.
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
Data Mining
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Author : Ian H. Witten
language : en
Publisher: Elsevier
Release Date : 2025-02-04
Data Mining written by Ian H. Witten and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Features in-depth information on deep learning and probabilistic models - Covers performance improvement techniques, including input preprocessing and combining output from different methods - Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software - Includes all-new exercises for each chapter
Java Data Mining Strategy Standard And Practice
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Author : Mark F. Hornick
language : en
Publisher: Elsevier
Release Date : 2010-07-26
Java Data Mining Strategy Standard And Practice written by Mark F. Hornick and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-26 with Computers categories.
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard. - Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems - JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects - JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API - Free, downloadable KJDM source code referenced in the book available here
Java Data Mining The Ultimate Step By Step Guide
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Author : Gerardus Blokdyk
language : en
Publisher:
Release Date : 2018
Java Data Mining The Ultimate Step By Step Guide written by Gerardus Blokdyk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic books categories.
Java Data Mining The Ultimate Step-By-Step Guide.
A Quick Guide To Data Mining With Weka And Java Using Weka
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Author : Eric Goh
language : en
Publisher: SVBook
Release Date :
A Quick Guide To Data Mining With Weka And Java Using Weka written by Eric Goh and has been published by SVBook this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.
This technical book aim to equip the reader with Weka, Data Mining in a fast and practical way. There will be many examples and explanations that are straight to the point. Contents 1. Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) 2. Getting Started (INstall Weka) 3. Prediction and Classification (Prediction and Classification) 4. Machine Learning Basics (KMeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) 5. Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) 6. Java interact Weka (Use Java to use Weka, in order to develop your own prediction or classification system) 7. Conclusion This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/machine-learning-with-java-and-weka/?couponCode=SPECIALCOUPON
Java For Data Science
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Author : Richard M. Reese
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-10
Java For Data Science written by Richard M. Reese and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-10 with Computers categories.
Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.
Text Mining
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Author : Taeho Jo
language : en
Publisher: Springer
Release Date : 2018-06-07
Text Mining written by Taeho Jo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-07 with Technology & Engineering categories.
This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.
Data Mining
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Author : 威腾
language : en
Publisher:
Release Date : 2003
Data Mining written by 威腾 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Data mining categories.