Data Mining Methods For Knowledge Discovery
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Data Mining Methods For Knowledge Discovery
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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Data Mining Methods For Knowledge Discovery written by Krzysztof J. Cios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.
Data Mining And Knowledge Discovery Technologies
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Author : Taniar, David
language : en
Publisher: IGI Global
Release Date : 2008-01-31
Data Mining And Knowledge Discovery Technologies written by Taniar, David and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-31 with Computers categories.
As information technology continues to advance in massive increments, the bank of information available from personal, financial, and business electronic transactions and all other electronic documentation and data storage is growing at an exponential rate. With this wealth of information comes the opportunity and necessity to utilize this information to maintain competitive advantage and process information effectively in real-world situations. Data Mining and Knowledge Discovery Technologies presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics.
Data Mining
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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05
Data Mining written by Krzysztof J. Cios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-05 with Computers categories.
“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.
Dynamic Integration Of Data Mining Methods In Knowledge Discovery Systems
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Author : Alexey Tsymbal
language : en
Publisher:
Release Date : 2002
Dynamic Integration Of Data Mining Methods In Knowledge Discovery Systems written by Alexey Tsymbal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
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Advanced Methods For Knowledge Discovery From Complex Data
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Author : Ujjwal Maulik
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-06
Advanced Methods For Knowledge Discovery From Complex Data written by Ujjwal Maulik and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-06 with Computers categories.
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.
Dynamic Integration Of Data Mining Methods In Knowledge Discovery Systems
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Author : Peter Kokol
language : en
Publisher:
Release Date : 2000
Dynamic Integration Of Data Mining Methods In Knowledge Discovery Systems written by Peter Kokol and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Methodologies For Knowledge Discovery And Data Mining
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Author : Ning Zhong
language : en
Publisher: Springer Science & Business Media
Release Date : 1999-04-14
Methodologies For Knowledge Discovery And Data Mining written by Ning Zhong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-04-14 with Computers categories.
This book constitutes the refereed proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '99, held in Beijing, China, in April 1999. The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization; causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies.
International Summer School On Knowledge Discovery In Databases And Data Mining Methods And Applications
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Author : Pavel B. Brazdil
language : en
Publisher:
Release Date : 1998
International Summer School On Knowledge Discovery In Databases And Data Mining Methods And Applications written by Pavel B. Brazdil and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Knowledge Discovery And Data Mining
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Author : O. Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Knowledge Discovery And Data Mining written by O. Maimon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Computers categories.
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).
Knowledge Discovery Process And Methods To Enhance Organizational Performance
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Author : Kweku-Muata Osei-Bryson
language : en
Publisher: CRC Press
Release Date : 2015-03-16
Knowledge Discovery Process And Methods To Enhance Organizational Performance written by Kweku-Muata Osei-Bryson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-16 with Business & Economics categories.
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 identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.