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Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
Total Pages: 740
Release: 2011-06-09
Genre: Computers
ISBN: 0123814804
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Management of Heterogeneous and Autonomous Database Systems

Management of Heterogeneous and Autonomous Database Systems
Author: Ahmed K. Elmagarmid
Publisher: Morgan Kaufmann
Total Pages: 440
Release: 1999
Genre: Computers
ISBN: 9781558602168
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An Overview of Multidatabase Systems: Past and Present / Athman Bouguettaya, Boualem Benatallah, Ahmed Elmagarmid / - Local Autonomy and Its Effects on Multidatabase Systems / Ahmed Elmagarmid, Weimin Du, Rafi Ahmed / - Semantic Similarities Between Objects in Multiple Databases / Vipul Kashyap, Amit Sheth / - Resolution of Representational Diversity in Multidatabase Systems / Joachim Hammer, Dennis McLeod / - Schema Integration: Past, Present, and Future / Sudha Ram, V. Ramesh / - Schema and Language Translation / Bogdan Czejdo, Le Gruenwald / - Multidatabase Languages / Paolo Missier, Marek Rusinkiewicz, W. Jin / - Interdependent Database Systems / George Karabatis, Marek Rusinkiewicz, Amit Sheth / - Correctness Criteria and Concurrency Control / Panos K. Chrysanthis, Krithi Ramamritham / - Transaction Management in Multidatabase Systems: Current Technologies and Formalisms / Ken Barker, Ahmed Elmagarmid / - Transaction-Based Recovery / Jari Veijalainen. ...

Morgan Kaufmann series in data management systems

Morgan Kaufmann series in data management systems
Author: Stefano Ceri
Publisher: Morgan Kaufmann
Total Pages: 596
Release: 2003
Genre: Computers
ISBN: 9781558608436
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This text represents a breakthrough in the process underlying the design of the increasingly common and important data-driven Web applications.

Data Mining

Data Mining
Author: Ian H. Witten
Publisher: Morgan Kaufmann
Total Pages: 655
Release: 2016-10-01
Genre: Computers
ISBN: 0128043571
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth 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 fourth 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 substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. 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 Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book

Data Mining

Data Mining
Author: Ian H. Witten
Publisher: Elsevier
Total Pages: 665
Release: 2011-02-03
Genre: Computers
ISBN: 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

Managing Reference Data in Enterprise Databases

Managing Reference Data in Enterprise Databases
Author: Malcolm Chisholm
Publisher: Morgan Kaufmann
Total Pages: 412
Release: 2001
Genre: Computers
ISBN: 9781558606975
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"This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time. It should be of interest to DBAs, data architects and modelers, programmers who have to write database programs, and yes, even managers. This book is a winner." - Karen Watterson, Editor SQL Server Professional "Malcolm Chisholm has produced a very readable book. It is well-written and with excellent examples. It will, I am sure, become the Reference Book on Reference Data." - Clive Finkelstein, "Father" of Information Engineering, Managing Director, Information Engineering Services Pty Ltd Reference data plays a key role in your business databases and must be free from defects of any kind. So why is it so hard to find information on this critical topic? Recognizing the dangers of taking reference data for granted, Managing Reference Data in Enterprise Databases gives you precisely what you've been seeking: A complete guide to the implementation and management of reference data of all kinds. This book begins with a thorough definition of reference data, then proceeds with a detailed examination of all reference data issues, fully describing uses, common difficulties, and practical solutions. Whether you're a database manager, architect, administrator, programmer, or analyst, be sure to keep this easy-to-use reference close at hand. Features Solves special challenges associated with maintaining reference data. Addresses a wide range of reference data issues, including acronyms, redundancy, mapping, life cycles, multiple languages, and querying. Describes how reference data interacts with other system components, what problems can arise, and how to mitigate these problems. Offers examples of standard reference data types and matrices for evaluating management methods. Provides a number of standard reference data tables and more specialized material to help you deal with reference data, via a companion Web site

Component Database Systems

Component Database Systems
Author: Klaus R. Dittrich
Publisher: Elsevier
Total Pages: 313
Release: 2000-10-25
Genre: Computers
ISBN: 0080490727
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Component Database Systems is a collection of invited chapters by the researchers making the most influential contributions in the database industry's trend toward componentization This book represents the sometimes-divergent, sometimes-convergent approaches taken by leading database vendors as they seek to establish commercially viable componentization strategies. Together, these contributions form the first book devoted entirely to the technical and architectural design of component-based database systems. In addition to detailing the current state of their research, the authors also take up many of the issues affecting the likely future directions of component databases. If you have a stake in the evolution of any of today's leading database systems, this book will make fascinating reading. It will also help prepare you for the technology that is likely to become widely available over the next several years. * Is comprised of contributions from the field's most highly respected researchers, including key figures at IBM, Oracle, Informix, Microsoft, and POET. * Represents the entire spectrum of approaches taken by leading software companies working on DBMS componentization strategies. * Covers component-focused architectures, methods for hooking components into an overall system, and support for component development. * Examines the component technologies that are most valuable to Web-based and multimedia databases. * Presents a thorough classification and overview of component database systems.

Data Preparation for Data Mining Using SAS

Data Preparation for Data Mining Using SAS
Author: Mamdouh Refaat
Publisher: Elsevier
Total Pages: 425
Release: 2010-07-27
Genre: Computers
ISBN: 0080491006
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Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little “how to information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. A complete framework for the data preparation process, including implementation details for each step. The complete SAS implementation code, which is readily usable by professional analysts and data miners. A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.

Information Modeling and Relational Databases

Information Modeling and Relational Databases
Author: Terry Halpin
Publisher: Elsevier
Total Pages: 1086
Release: 2024-07-22
Genre: Mathematics
ISBN: 0443237913
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Information Modeling and Relational Databases, Third Edition, provides an introduction to ORM (Object-Role Modeling) and much more. In fact, it is the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. This book is intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, and programmers. Dr. Terry Halpin and Dr. Tony Morgan, pioneers in the development of ORM, blend conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. The all-new Third Edition includes coverage of advances and improvements in ORM and UML, nominalization, relational mapping, SQL, XML, data interchange, NoSQL databases, ontological modeling, and post-relational databases. Supported by examples, exercises, and useful background information, the authors’ step-by-step approach teaches you to develop a natural-language-based ORM model, and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives. "This book is an excellent introduction to both information modeling in ORM and relational databases. The book is very clearly written in a step-by-step manner and contains an abundance of well-chosen examples illuminating practice and theory in information modeling. I strongly recommend this book to anyone interested in conceptual modeling and databases." — Dr. Herman Balsters, Director of the Faculty of Industrial Engineering, University of Groningen, The Netherlands Presents the most in-depth coverage of object-role modeling, including a thorough update of the book for the latest versions of ORM, ER, UML, OWL, and BPMN modeling. Includes clear coverage of relational database concepts as well as the latest developments in SQL, XML, information modeling, data exchange, and schema transformation. Case studies and a large number of class-tested exercises are provided for many topics. Includes all-new chapters on data file formats and NoSQL databases.

Data on the Web

Data on the Web
Author: Serge Abiteboul
Publisher: Morgan Kaufmann
Total Pages: 280
Release: 2000
Genre: Computers
ISBN: 9781558606227
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Data model. Queries. Types. Sysems. A syntax for data. XML.. Query languages. Query languages for XML. Interpretation and advanced features. Typing semistructured data. Query processing. The lore system. Strudel. Database products supporting XML. Bibliography. Index. About the authors.