Java Data Mining The Ultimate Step By Step Guide
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| Author | : Ian H. Witten |
| Publisher | : Elsevier |
| Total Pages | : 665 |
| Release | : 2011-02-03 |
| Genre | : Computers |
| ISBN | : 0080890369 |
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
| Author | : Mark F. Hornick |
| Publisher | : Elsevier |
| Total Pages | : 545 |
| Release | : 2010-07-26 |
| Genre | : Computers |
| ISBN | : 0080495915 |
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
| Author | : Charu C. Aggarwal |
| Publisher | : Springer |
| Total Pages | : 746 |
| Release | : 2015-04-13 |
| Genre | : Computers |
| ISBN | : 3319141422 |
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
| Author | : Robert Layton |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 344 |
| Release | : 2015-07-29 |
| Genre | : Computers |
| ISBN | : 1784391204 |
The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.
| Author | : Parteek Bhatia |
| Publisher | : Cambridge University Press |
| Total Pages | : 514 |
| Release | : 2019-06-27 |
| Genre | : Computers |
| ISBN | : 110858585X |
Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.
| Author | : Dr. Harald Gunia |
| Publisher | : Orange Education Pvt Ltd |
| Total Pages | : 695 |
| Release | : 2023-11-20 |
| Genre | : Computers |
| ISBN | : 8119416406 |
Empowering the Global Workforce with ChatGPT Expertise. KEY FEATURES ● Comprehensive Guide to GPT Evolution, AI Capabilities, and Prompt Engineering. ● Design Patterns for Enterprise Personas, Architectures, and AI Assistants. ● Management of the GPT Solution Development Cycle. DESCRIPTION “Ultimate ChatGPT Handbook for Enterprises” is your indispensable resource for navigating the transformative world of ChatGPT within the enterprise domain. It provides a deep dive into ChatGPT's evolution, capabilities, and its potential to democratize technology interactions through natural language. Throughout its chapters, you'll embark on a journey that spans from comprehending the lineage of GPT models to mastering advanced prompt engineering techniques. It will help you take a step into a futuristic enterprise landscape where ChatGPT seamlessly collaborates with human intelligence, fundamentally transforming daily work routines across various enterprise roles. The latter chapters will help you attain proficiency in managing GPT projects, discovering the agile and iterative approach to GPT solution life cycles using real-world scenarios. You will also be introduced to practical GPT implementation frameworks for both Python and Java. This book offers practical insights and applicable skills, fostering informed dialogue and active participation in the ongoing enterprise AI revolution. If you want to stay at the forefront of the rapidly evolving AI landscape and unlock enterprise excellence through ChatGPT, this book is your go-to companion. WHAT WILL YOU LEARN ● Discover strategies to maximize ChatGPT's capabilities, fostering innovation and process optimization across global industry sectors. ● Develop proficiency in crafting effective prompts using Prompt Engineering for seamless AI interactions, enhancing ChatGPT's utility in enterprise contexts. ● Acquire the expertise to design intelligent assistants that elevate enterprise operations, promoting efficiency and innovation. ● Gain practical skills to implement ChatGPT solutions using Python and Java, enabling seamless integration with your enterprise systems. ● Learn effective project management from initiation to validation and change management, ensuring successful GPT solution implementation in enterprises. ● Explore how ChatGPT can reshape various roles, boosting productivity and fostering harmonious AI-human collaboration in the workplace. WHO IS THIS BOOK FOR? This book is designed for business professionals, IT specialists, and AI enthusiasts who are eager to delve into the transformative world of ChatGPT and its applications in the enterprise landscape. A foundational understanding of AI concepts and familiarity with enterprise dynamics will be beneficial, but not mandatory, as the book is structured to guide readers from basic concepts to advanced implementations, catering to both novices and experts alike. TABLE OF CONTENTS 1. From GPT-1 to ChatGPT-4: The Evolution Towards Generative AI 2. CapabilityGPT An Enterprise AI-Capability Framework for ChatGPT 3. The Impact of ChatGPT on the Enterprise 4. Architecture Patterns enabled by GPT-Models 5. Advanced GPT Prompt Engineering Techniques 6. Designing Prompt-based Intelligent Assistants 7. Mastery of GPT-Projects 8. LangChain: GPT Implementation Framework for Python 9. predictive-powers: GPT Implementation Framework for Java APPENDIX A: APPENDIX B:
| Author | : Jared Dean |
| Publisher | : John Wiley & Sons |
| Total Pages | : 293 |
| Release | : 2014-05-07 |
| Genre | : Computers |
| ISBN | : 1118920708 |
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
| Author | : Jure Leskovec |
| Publisher | : Cambridge University Press |
| Total Pages | : 480 |
| Release | : 2014-11-13 |
| Genre | : Computers |
| ISBN | : 1107077230 |
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
| Author | : Hong Zhou |
| Publisher | : Apress |
| Total Pages | : 223 |
| Release | : 2020-06-13 |
| Genre | : Computers |
| ISBN | : 1484259823 |
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approachBuild on a theoretical introduction of a data mining method, followed by an Excel implementationUnveil the mystery behind machine learning algorithms, making a complex topic accessible to everyoneBecome skilled in creative uses of Excel formulas and functionsObtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.
| Author | : Hadley Wickham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 372 |
| Release | : 2021-04-29 |
| Genre | : Computers |
| ISBN | : 149204735X |
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production
