Author: Ian H. Witten
Publisher: Morgan Kaufmann
Release Date: 2016-10-01
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 http://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
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Author: Ian H. Witten
Release Date: 2011-02-03
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
Joe Celkos SQL for Smarties: Advanced SQL Programming offers tips and techniques in advanced programming. This book is the fourth edition and it consists of 39 chapters, starting with a comparison between databases and file systems. It covers transactions and currency control, schema level objects, locating data and schema numbers, base tables, and auxiliary tables. Furthermore, procedural, semi-procedural, and declarative programming are explored in this book. The book also presents the different normal forms in database normalization, including the first, second, third, fourth, fifth, elementary key, domain-key, and Boyce-Codd normal forms. It also offers practical hints for normalization and denormalization. The book discusses different data types, such as the numeric, temporal and character data types; the different predicates; and the simple and advanced SELECT statements. In addition, the book presents virtual tables, and it discusses data partitions in queries; grouping operations; simple aggregate functions; and descriptive statistics, matrices and graphs in SQL. The book concludes with a discussion about optimizing SQL. It will be of great value to SQL programmers. Expert advice from a noted SQL authority and award-winning columnist who has given ten years service to the ANSI SQL standards committee Teaches scores of advanced techniques that can be used with any product, in any SQL environment, whether it is an SQL 92 or SQL 2008 environment Offers tips for working around deficiencies and gives insight into real-world challenges
Database Modeling and Design, Fourth Edition, the extensively revised edition of the classic logical database design reference, explains how you can model and design your database application in consideration of new technology or new business needs. It is an ideal text for a stand-alone data management course focused on logical database design, or a supplement to an introductory text for introductory database management. This book features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to any SQL-based system. The common examples are based on real-life experiences and have been thoroughly class-tested. The text takes a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling - complemented with examples for both approaches. It also discusses the use of data modeling concepts in logical database design; the transformation of the conceptual model to the relational model and to SQL syntax; the fundamentals of database normalization through the fifth normal form; and the major issues in business intelligence such as data warehousing, OLAP for decision support systems, and data mining. There are examples for how to use the most popular CASE tools to handle complex data modeling problems, along with exercises that test understanding of all material, plus solutions for many exercises. Lecture notes and a solutions manual are also available. This edition will appeal to professional data modelers and database design professionals, including database application designers, and database administrators (DBAs); new/novice data management professionals, such as those working on object oriented database design; and students in second courses in database focusing on design. + a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling--with examples throughout the book in both approaches! + the details and examples of how to use data modeling concepts in logical database design, and the transformation of the conceptual model to the relational model and to SQL syntax; + the fundamentals of database normalization through the fifth normal form; + practical coverage of the major issues in business intelligence--data warehousing, OLAP for decision support systems, and data mining; + examples for how to use the most popular CASE tools to handle complex data modeling problems. + Exercises that test understanding of all material, plus solutions for many exercises.
Author: Jim Melton
Publisher: Morgan Kaufmann
Release Date: 2011-04-08
XML has become the lingua franca for representing business data, for exchanging information between business partners and applications, and for adding structure– and sometimes meaning—to text-based documents. XML offers some special challenges and opportunities in the area of search: querying XML can produce very precise, fine-grained results, if you know how to express and execute those queries. For software developers and systems architects: this book teaches the most useful approaches to querying XML documents and repositories. This book will also help managers and project leaders grasp how “querying XML fits into the larger context of querying and XML. Querying XML provides a comprehensive background from fundamental concepts (What is XML?) to data models (the Infoset, PSVI, XQuery Data Model), to APIs (querying XML from SQL or Java) and more. * Presents the concepts clearly, and demonstrates them with illustrations and examples; offers a thorough mastery of the subject area in a single book. * Provides comprehensive coverage of XML query languages, and the concepts needed to understand them completely (such as the XQuery Data Model). * Shows how to query XML documents and data using: XPath (the XML Path Language); XQuery, soon to be the new W3C Recommendation for querying XML; XQuery's companion XQueryX; and SQL, featuring the SQL/XML * Includes an extensive set of XQuery, XPath, SQL, Java, and other examples, with links to downloadable code and data samples.
Author: Joe Celko
Publisher: Morgan Kaufmann
Release Date: 2009-09-22
Joe Celko has looked deep into the code of SQL programmers and found a consistent and troubling pattern - a frightening lack of consistency between their individual encoding schemes and those of the industries in which they operate. This translates into a series of incompatible databases, each one an island unto itself that is unable to share information with others in an age of internationalization and business interdependence. Such incompatibility severely hinders information flow and the quality of company data. Data, Measurements and Standards in SQL reveals the shift these programmers need to make to overcome this deadlock. By collecting and detailing the diverse standards of myriad industries, and then giving a declaration for the units that can be used in an SQL schema, Celko enables readers to write and implement portable data that can interface to any number of external application systems! This book doesn't limit itself to one subject, but serves as a detailed synopsis of measurement scales and data standards for all industries, thereby giving RDBMS programmers and designers the knowledge and know-how they need to communicate effectively across business boundaries. * Collects and details the diverse data standards of myriad industries under one cover, thereby creating a definitive, one-stop-shopping opportunity for database programmers. * Enables readers to write and implement portable data that can interface to any number external application systems, allowing readers to cross business boundaries and move up the career ladder. * Expert advice from one of the most-read SQL authors in the world who is well known for his ten years of service on the ANSI SQL standards committee and Readers Choice Award winning column in Intelligent Enterprise.
Author: Thomas A. Runkler
Release Date: 2010-02-24
Dieses Buch behandelt die wichtigsten Methoden zur Erkennung und Extraktion von „Wissen“ aus numerischen und nichtnumerischen Datenbanken in Technik und Wirtschaft. Es vermittelt einen kompakten, fundierten Überblick über die verschiedenen Methoden sowie deren Motivation und versetzt den Leser in die Lage, Data Mining selbst praktisch einzusetzen.
Author: Viktor Mayer-Schönberger
Publisher: Redline Wirtschaft
Release Date: 2013-10-08
Genre: Political Science
Ob Kaufverhalten, Grippewellen oder welche Farbe am ehesten verrät, ob ein Gebrauchtwagen in einem guten Zustand ist – noch nie gab es eine solche Menge an Daten und noch nie bot sich die Chance, durch Recherche und Kombination in der Daten¬flut blitzschnell Zusammenhänge zu entschlüsseln. Big Data bedeutet nichts weniger als eine Revolution für Gesellschaft, Wirtschaft und Politik. Es wird die Weise, wie wir über Gesundheit, Erziehung, Innovation und vieles mehr denken, völlig umkrempeln. Und Vorhersagen möglich machen, die bisher undenkbar waren. Die Experten Viktor Mayer-Schönberger und Kenneth Cukier beschreiben in ihrem Buch, was Big Data ist, welche Möglichkeiten sich eröffnen, vor welchen Umwälzungen wir alle stehen – und verschweigen auch die dunkle Seite wie das Ausspähen von persönlichen Daten und den drohenden Verlust der Privatsphäre nicht.
Maschinelles Lernen heißt, Computer so zu programmieren, dass ein bestimmtes Leistungskriterium anhand von Beispieldaten und Erfahrungswerten aus der Vergangenheit optimiert wird. Das vorliegende Buch diskutiert diverse Methoden, die ihre Grundlagen in verschiedenen Themenfeldern haben: Statistik, Mustererkennung, neuronale Netze, Künstliche Intelligenz, Signalverarbeitung, Steuerung und Data Mining. In der Vergangenheit verfolgten Forscher verschiedene Wege mit unterschiedlichen Schwerpunkten. Das Anliegen dieses Buches ist es, all diese unterschiedlichen Ansätze zu kombinieren, um eine allumfassende Behandlung der Probleme und ihrer vorgeschlagenen Lösungen zu geben.