Knowledge Discovery for Business Information Systems

Knowledge Discovery for Business Information Systems

Author: Witold Abramowicz

Publisher: Springer Science & Business Media

Published: 2006-04-18

Total Pages: 442

ISBN-13: 030646991X

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Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing. To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis. Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA.


Data Mining Methods for Knowledge Discovery

Data Mining Methods for Knowledge Discovery

Author: Krzysztof J. Cios

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 508

ISBN-13: 1461555892

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Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.


Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining

Author: O. Maimon

Publisher: Springer Science & Business Media

Published: 2000-12-31

Total Pages: 192

ISBN-13: 9780792366478

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This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).


The Data Bonanza

The Data Bonanza

Author: Malcolm Atkinson

Publisher: John Wiley & Sons

Published: 2013-03-19

Total Pages: 423

ISBN-13: 1118540301

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Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.


Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining

Author: Usama M. Fayyad

Publisher:

Published: 1996

Total Pages: 638

ISBN-13:

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Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.


Innovative Applications of Knowledge Discovery and Information Resources Management

Innovative Applications of Knowledge Discovery and Information Resources Management

Author: Swayze, Susan

Publisher: IGI Global

Published: 2018-06-01

Total Pages: 348

ISBN-13: 1522558306

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Technological advancements have become an integral part of life, impacting the way we work, communicate, make decisions, learn, and play. As technology continually progresses, humans are being outpaced by its capabilities, and it is important for businesses, organizations, and individuals to understand how to optimize data and to implement new methods for more efficient knowledge discovery and information management and retrieval. Innovative Applications of Knowledge Discovery and Information Resources Management offers in-depth coverage on the pervasiveness of technological change with a collection of material on topics such as the impact of permeable work-life boundaries, burnout and turnover, big data usage, and computer-based learning. It proves a worthy source for academicians, practitioners, IT leaders, IT professionals, and advanced-level students interested in examining the ways in which technology is changing the world.


Knowledge Discovery Process and Methods to Enhance Organizational Performance

Knowledge Discovery Process and Methods to Enhance Organizational Performance

Author: Kweku-Muata Osei-Bryson

Publisher: CRC Press

Published: 2015-03-16

Total Pages: 404

ISBN-13: 1482212382

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Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id


Business Information Systems

Business Information Systems

Author: Witold Abramowicz

Publisher: Springer

Published: 2012-02-02

Total Pages: 313

ISBN-13: 3642218636

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This book contains the refereed proceedings of the 14th International Conference on Business Information Systems, BIS 2011, held in Poznań, Poland, in June 2011. The 25 revised full papers were carefully reviewed and selected from 57 submissions. Following this year's conference theme of "Towards Flexible, Personalized and Adaptive Business Applications," the contributions were grouped into eight sections on business rules, business process verification, business process variants and composition, business process improvement, data modeling and integration, Internet science, modern enterprises, and specific business information systems issues.