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Author: Pradeep Kumar
Publisher:
Published: 2011-07-01
Total Pages: 272
ISBN-13: 9781613500583
DOWNLOAD EBOOK"This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Author: Sankar K. Pal
Publisher: CRC Press
Published: 2004-05-27
Total Pages: 275
ISBN-13: 1135436401
DOWNLOAD EBOOKPattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
Author: Norwood Russell Hanson
Publisher: CUP Archive
Published: 1979
Total Pages: 260
ISBN-13:
DOWNLOAD EBOOKAuthor: Mark R. Anderson
Publisher: FiReBooks
Published: 2017-11-20
Total Pages: 240
ISBN-13: 9780996725446
DOWNLOAD EBOOKRenowned technology and economics forecaster Mark Anderson reveals hidden patterns beneath the art and science of predicting the future. Through a series of personal vignettes, Anderson exposes a complex web of causes, influences, and effects that propel today's world, then describes strategies that he employs to lay bare new trends, to make new discoveries in a wide variety of disciplines, and to accurately foresee future events.
Author: Xue Z. Wang
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 263
ISBN-13: 1447104218
DOWNLOAD EBOOKModern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
Author: Krzysztof J. Cios
Publisher: Springer Science & Business Media
Published: 2007-10-05
Total Pages: 601
ISBN-13: 0387367950
DOWNLOAD EBOOKThis comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
Author: Theophano Mitsa
Publisher: CRC Press
Published: 2010-03-10
Total Pages: 398
ISBN-13: 1420089773
DOWNLOAD EBOOKFrom basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Author: Laura Elnitski
Publisher: World Scientific
Published: 2011
Total Pages: 236
ISBN-13: 9814327727
DOWNLOAD EBOOKMapping the genomic landscapes is one of the most exciting frontiers of science. We have the opportunity to reverse engineer the blueprints and the control systems of living organisms. Computational tools are key enablers in the deciphering process. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. The first section of the book discusses methods for discovering patterns in DNA and RNA. This is followed by the second section that reflects on methods in various ways, including performance, usage and paradigms.
Author: Usama M. Fayyad
Publisher: Morgan Kaufmann
Published: 2002
Total Pages: 446
ISBN-13: 9781558606890
DOWNLOAD EBOOKThis text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Author: Lutz H. Hamel
Publisher: John Wiley & Sons
Published: 2011-09-20
Total Pages: 211
ISBN-13: 1118211030
DOWNLOAD EBOOKAn easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
