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Data Mining

Author: Ian H. Witten
Publisher: Morgan Kaufmann
ISBN: 0128043571
Size: 51.88 MB
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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 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 Southeast Asia Edition

Author: Jiawei Han
Publisher: Elsevier
ISBN: 9780080475585
Size: 15.63 MB
Format: PDF
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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 companion site

Joe Celko S Sql For Smarties

Author: Joe Celko
Publisher: Elsevier
ISBN: 9780123820235
Size: 27.75 MB
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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

Author: Toby J. Teorey
Publisher: Elsevier
ISBN: 9780080470771
Size: 56.41 MB
Format: PDF, Docs
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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.

Querying Xml

Author: Jim Melton
Publisher: Morgan Kaufmann
ISBN: 9780080540160
Size: 61.64 MB
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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.

Perspectives In Business Informatics Research

Author: Björn Johansson
Publisher: Springer
ISBN: 3319649302
Size: 23.98 MB
Format: PDF, Docs
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This book constitutes the proceedings of the 16th International Conference on Perspectives in Business Informatics Research, BIR 2017, held in Copenhagen, Denmark, in August 2017. This year the BIR conference attracted 59 submissions from 23 countries. They were reviewed by 45 members of the Program Committee, and as a result, 17 full papers and 3 short papers were selected for presentation at the conference and publication in this volume. They are organized in sections on enterprise architecture, business process management, business analytics, information systems applications, and information systems development. In addition, the summaries of the two conference keynotes are also included. This year, the conference theme was the digital transformation, which will impact most businesses, organizations and societies and call for new and radical approaches to how we adopt, use and manage IT.

Data Mining Practical Machine Learning Tools And Techniques 4th Ed Morgan Kaurmann Elsevier 2017

Author: Witten - Frank - Hall - Pal
Publisher: Bukupedia
ISBN: 0128042915
Size: 12.85 MB
Format: PDF, Mobi
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The convergence of computing and communication has produced a society that feeds on information. Yet most of the information is in its raw form: data. If data is characterized as recorded facts, then information is the set of patterns, or expectations, that underlie the data. There is a huge amount of information locked up in databases—information that is potentially important but has not yet been discovered or articulated. Our mission is to bring it forth. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Of course, there will be problems. Many patterns will be banal and uninteresting. Others will be spurious, contingent on accidental coincidences in the particular dataset used. And real data is imperfect: some parts will be garbled, some missing. Anything that is discovered will be inexact: there will be exceptions to every rule and cases not covered by any rule. Algorithms need to be robust enough to cope with imperfect data and to extract regularities that are inexact but useful. Machine learning provides the technical basis of data mining. It is used to extract information from the raw data in databases—information i.e., ideally, expressed in a comprehensible form and can be used for a variety of purposes. The process is one of abstraction: taking the data, warts and all, and inferring whatever structure underlies it. This book is about the tools and techniques of machine learning that are used in practical data mining for finding, and if possible describing, structural patterns in data. As with any burgeoning new technology that enjoys intense commercial attention, the use of machine learning is surrounded by a great deal of hype in the technical—and sometimes the popular—press. Exaggerated reports appear of the secrets that can be uncovered by setting learning algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of simple and practical techniques that can often extract useful information from raw data. This book describes these techniques and shows how they work. In many applications machine learning enables the acquisition of structural descriptions from examples. The kind of descriptions that are found can be used for prediction, explanation, and understanding. Some data mining applications focus on prediction: forecasting what will happen in new situations from data that describe what happened in the past, often by guessing the classification of new examples. But we are equally—perhaps more—interested in applications where the result of “learning” is an actual description of a structure that can be used to classify examples. This structural description supports explanation and understanding as well as prediction. In our experience, insights gained by the user are xxiii

Digitale Transformation Von Gesch Ftsmodellen

Author: Daniel Schallmo
Publisher: Springer-Verlag
ISBN: 3658123885
Size: 31.20 MB
Format: PDF, Kindle
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Dieses Buch zeigt wie es Unternehmen gelingt Ihre Geschäftsmodelle auf die Digitale Zukunft vorzubereiten und wie dadurch Wettbewerbsvorteile geschaffen und Kundenanforderungen besser erfüllt werden können. Die Autoren aus Praxis und Wissenschaft zeigen, wie die Digitale Transformation von Unternehmen über die gesamte Wertschöpfungskette hinweg gelingt. Die Beiträge behandeln Ansätze und Instrumente, Studienergebnisse und Best Practices unterschiedlicher Industrien im Kontext der Digitalen Transformation. Die Inhalte berücksichtigen divergierende Anforderungen von Unternehmen und Industrien und können nach Bedarf kombiniert und erweitert werden, um sie an die spezifischen Rahmenbedingungen eines Unternehmens anzupassen.

Wissensbasierte Diagnosesysteme Im Service Support

Author: Frank Puppe
Publisher: Springer-Verlag
ISBN: 3642569293
Size: 24.18 MB
Format: PDF
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Industriepraktiker, Informatiker und Soziologen berichten über ihre Konzepte und Erfahrungen bei der Entwicklung und dem Einsatz wissensbasierter Diagnosesysteme im Service-Support. Dabei wird Fachwissen zur Lösung von Kundenproblemen formalisiert und ggf. mit elektronisch bereits vorhandenen Dokumenten wie z.B. Handbüchern verknüpft. Je nach betrieblicher Situation können Kunden so direkt über das Internet oder über die Hotline eines Intranets bei der Fehlerbehebung unterstützt werden. Der Hauptaufwand besteht in der Entwicklung und Pflege der Wissensbasis. Um ihn zu verringern, werden eine Reihe bekannter und neuer Strategien vorgestellt. Dazu gehören Wissensformalisierungsmuster, Wissensmodularisierung durch kooperierende Diagnoseagenten, Selbstakquisition und begrenzte Systemeinsätze zur betrieblichen Einführung. Die beigelegte CD-ROM enthält zwei Tutorials und eine Demo-Version des Diagnostik-Shellbaukastens D3, mit der die Leser mühelos selber Prototypen entwickeln können.