Mathematical Methods For Knowledge Discovery And Data Mining
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Mathematical Methods For Knowledge Discovery And Data Mining
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Author : Felici, Giovanni
language : en
Publisher: IGI Global
Release Date : 2007-10-31
Mathematical Methods For Knowledge Discovery And Data Mining written by Felici, Giovanni and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-31 with Computers categories.
"This book focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications"--Provided by publisher.
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.
Advanced Techniques In Knowledge Discovery And Data Mining
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Author : Nikhil Pal
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-31
Advanced Techniques In Knowledge Discovery And Data Mining written by Nikhil Pal 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 2007-12-31 with Computers categories.
Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.
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 Via Logic Based Methods
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Author : Evangelos Triantaphyllou
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-08
Data Mining And Knowledge Discovery Via Logic Based Methods written by Evangelos Triantaphyllou 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 2010-06-08 with Computers categories.
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.
Foundations Of Data Mining And Knowledge Discovery
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Author : Tsau Young Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-09-02
Foundations Of Data Mining And Knowledge Discovery written by Tsau Young Lin 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 2005-09-02 with Computers categories.
"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.
Machine Learning For Data Science Handbook
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Author : Lior Rokach
language : en
Publisher: Springer Nature
Release Date : 2023-08-17
Machine Learning For Data Science Handbook written by Lior Rokach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Mathematics categories.
This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students’ feedback. This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science. Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role. This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.
Advances In Knowledge Discovery And Data Mining
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Author : David Cheung
language : en
Publisher: Springer
Release Date : 2003-06-29
Advances In Knowledge Discovery And Data Mining written by David Cheung and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This book constitutes the refereed proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, held in Hong Kong, China in April 2001. The 38 revised full papers and 22 short papers presented were carefully reviewed and selected from a total of 152 submissions. The book offers topical sections on Web mining, text mining, applications and tools, concept hierarchies, feature selection, interestingness, sequence mining, spatial and temporal mining, association mining, classification and rule induction, clustering, and advanced topics and new methods.
Proceedings Of The Third International Conference On Knowledge Discovery And Data Mining
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Author : David Heckerman
language : en
Publisher:
Release Date : 1997
Proceedings Of The Third International Conference On Knowledge Discovery And Data Mining written by David Heckerman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
Data Warehousing Data Mining
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Author : Dr. B. Shadaksharappa
language : en
Publisher: Book Rivers
Release Date : 2022-02-01
Data Warehousing Data Mining written by Dr. B. Shadaksharappa and has been published by Book Rivers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-01 with Antiques & Collectibles categories.
In the modern age of artificial intelligence and business analytics, data is considered as the oil of this cyber world. The mining of data has huge potential to improve business outcomes and to carry out the mining of data there is a growing demand for database mining experts. This book intends training learners to fill this gap. This book will give learners sufficient information to acquire mastery over the subject. It covers the practical aspects of data mining, data warehousing in a simplified manner without compromising on the details of the subject. The main strength of the book is the illustration of concepts with practical examples so that the learners can grasp the contents easily. Another important feature of the book is illustration of data mining algorithms with real life examples.