Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Uncertainty Modeling For Data Mining
Download Uncertainty Modeling For Data Mining full books in PDF, epub, and Kindle. Read online Uncertainty Modeling For Data Mining ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Uncertainty Modeling for Data Mining by : Zengchang Qin
Download or read book Uncertainty Modeling for Data Mining written by Zengchang Qin and published by Springer. This book was released on 2014-10-30 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning. Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
Author :Beihang University Zhixing Building Room 117 Publisher :Springer ISBN 13 :9783662520208 Total Pages :312 pages Book Rating :4.5/5 (22 download)
Book Synopsis Uncertainty Modeling for Data Mining by : Beihang University Zhixing Building Room 117
Download or read book Uncertainty Modeling for Data Mining written by Beihang University Zhixing Building Room 117 and published by Springer. This book was released on 2016-05-01 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.
Book Synopsis Uncertainty Modelling in Data Science by : Sébastien Destercke
Download or read book Uncertainty Modelling in Data Science written by Sébastien Destercke and published by Springer. This book was released on 2018-07-24 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.
Book Synopsis Uncertainty Analysis with High Dimensional Dependence Modelling by : Dorota Kurowicka
Download or read book Uncertainty Analysis with High Dimensional Dependence Modelling written by Dorota Kurowicka and published by John Wiley & Sons. This book was released on 2006-10-02 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.
Author :Michalis Vazirgiannis Publisher :Springer Science & Business Media ISBN 13 :144710031X Total Pages :231 pages Book Rating :4.4/5 (471 download)
Book Synopsis Uncertainty Handling and Quality Assessment in Data Mining by : Michalis Vazirgiannis
Download or read book Uncertainty Handling and Quality Assessment in Data Mining written by Michalis Vazirgiannis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
Book Synopsis Applied Research in Uncertainty Modeling and Analysis by : Bilal M. Ayyub
Download or read book Applied Research in Uncertainty Modeling and Analysis written by Bilal M. Ayyub and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
Book Synopsis Modeling Uncertainty in the Earth Sciences by : Jef Caers
Download or read book Modeling Uncertainty in the Earth Sciences written by Jef Caers and published by John Wiley & Sons. This book was released on 2011-05-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.
Book Synopsis Integrated Uncertainty in Knowledge Modelling and Decision Making by : Van-Nam Huynh
Download or read book Integrated Uncertainty in Knowledge Modelling and Decision Making written by Van-Nam Huynh and published by Springer. This book was released on 2015-10-08 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2015, held in Nha Trang, Vietnam, in October 2015. The 40 revised full papers were carefully reviewed and selected from 58 submissions and are presented together with three keynote and invited talks. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management
Book Synopsis Uncertainty Modeling in Knowledge Engineering and Decision Making by :
Download or read book Uncertainty Modeling in Knowledge Engineering and Decision Making written by and published by World Scientific. This book was released on 2012 with total page 1373 pages. Available in PDF, EPUB and Kindle. Book excerpt: FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view. Sample Chapter(s). Foreword (55 KB). Evaluation of Manufacturing Technology of Photovoltaic Cells (124 KB). Contents: Decision Making and Decision Support Systems; Uncertainty Modeling; Foundations of Computational Intelligence; Statistics, Data Analysis and Data Mining; Intelligent Information Processing; Productivity and Reliability; Applied Research. Readership: Graduate students, researchers, and academics in artificial intelligence/machine learning, information management, decision sciences, databases/information sciences and fuzzy logic.
Book Synopsis Uncertainty Quantification and Stochastic Modeling with Matlab by : Eduardo Souza de Cursi
Download or read book Uncertainty Quantification and Stochastic Modeling with Matlab written by Eduardo Souza de Cursi and published by Elsevier. This book was released on 2015-04-09 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlab® illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study. Discusses the main ideas of Stochastic Modeling and Uncertainty Quantification using Functional Analysis Details listings of Matlab® programs implementing the main methods which complete the methodological presentation by a practical implementation Construct your own implementations from provided worked examples
Book Synopsis Advances in Knowledge Discovery and Data Mining by : Wee Keong Ng
Download or read book Advances in Knowledge Discovery and Data Mining written by Wee Keong Ng and published by Springer Science & Business Media. This book was released on 2006-03-31 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.
Download or read book Data Mining written by and published by BoD – Books on Demand. This book was released on 2022-03-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.
Book Synopsis Uncertainty Modelling and Analysis by : Bilal M. Ayyub
Download or read book Uncertainty Modelling and Analysis written by Bilal M. Ayyub and published by Elsevier Publishing Company. This book was released on 1994 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vital information on machine intelligence and pattern recognition is provided by this publication. In particular, the 31 papers discuss the ways in which uncertainty modelling and analysis are becoming an integral part of system definition and modelling in many fields. Contributions are sourced from an international base of researchers, scientists and engineers working on theoretical developments and diversified applications in engineering systems. The book is divided into two main parts. The first, Uncertainty Models and Measures, includes chapters on theoretical studies and developments carried out on uncertainty (including cognitive uncertainty and how it relates to information and intelligence), information, fuzzy logic, expert systems and neural networks. There are also chapters on modelling uncertainty in the reliability assessment of complex systems, linguistic connectives, the principle of maximum buoyancy, uncertain evidence, inductive learning, convex modelling, new uncertainty measures and information and uncertainty.The larger second part, Applications to Engineering Systems, contains application-oriented studies in fields related to civil, electrical, energy and general engineering systems. The papers cover studies on general uncertainty types in structural engineering, bridges, transmission structures, structural reliability, structural identification, system life cycle analysis, control, construction activities, decision analysis, signal detection, risk management, product quality, military command and control, data bases, long-term projections and predictions and assessment of insurance indices.The book conveys the excitement, advances and promises that all these fields offer to our expanding information-based technological society. It also hopes to stimulate the interest of other researchers around the world who are facing the challenge of new theoretical studies and innovative technological changes.
Book Synopsis Uncertainty Modeling by : Vladik Kreinovich
Download or read book Uncertainty Modeling written by Vladik Kreinovich and published by Springer. This book was released on 2017-01-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.
Download or read book Data Science written by Ivo D. Dinov and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-12-06 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.
Book Synopsis Data Clustering by : Charu C. Aggarwal
Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Book Synopsis Managing and Mining Uncertain Data by : Charu C. Aggarwal
Download or read book Managing and Mining Uncertain Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-07-08 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
