Proceedings of the Seventh SIAM International Conference on Data Mining PDF Download
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Author: Chid Apte
Publisher: Proceedings in Applied Mathema
ISBN:
Category : Computers
Languages : en
Pages : 674
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Book Description
The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.
Author: Chid Apte
Publisher: Proceedings in Applied Mathema
ISBN:
Category : Computers
Languages : en
Pages : 674
Get Book Here
Book Description
The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.
Author: Joydeep Ghosh
Publisher: SIAM
ISBN: 9780898716115
Category : Computers
Languages : en
Pages : 662
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Book Description
The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.
Author: Uwe Naumann
Publisher: CRC Press
ISBN: 1439827354
Category : Computers
Languages : en
Pages : 602
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Book Description
Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems. The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs. Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific computing.
Author:
Publisher:
ISBN:
Category : Data mining
Languages : en
Pages : 674
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Book Description
Author: Bing Liu
Publisher: Springer Science & Business Media
ISBN: 3642194605
Category : Computers
Languages : en
Pages : 637
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Book Description
Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Author: Bernhard Ganter
Publisher: Springer Nature
ISBN: 3031634225
Category : Comprehension (Theory of knowledge)
Languages : en
Pages : 375
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Book Description
Formal Concept Analysis is a field of applied mathematics based on the mathematization of concept and conceptual hierarchy. It thereby activates mathematical thinking for conceptual data analysis and knowledge processing. The underlying notion of "concept" evolved early in the philosophical theory of concepts and still has effects today. In mathematics it played a special role during the emergence of mathematical logic in the 19th century. Subsequently, however, it had virtually no impact on mathematical thinking. It was not until 1979 that the topic was revisited and treated more thoroughly. Since then, Formal Concept Analysis has fully emerged, sparking a multitude of publications for which the first edition of this textbook established itself as the standard reference in the literature, with a total of 10000+ citations. This is the second edition, revised and extended, of the textbook published originally in German (1996) and translated into English (1999), giving a systematic presentation of the mathematical foundations while also focusing on their possible applications for data analysis and knowledge processing. In times of digital knowledge processing, formal methods of conceptual analysis are gaining in importance. The book makes the basic theory for such methods accessible in a compact form, and presents graphical methods for representing concept systems that have proved themselves essential in communicating knowledge. The textbook complements each chapter with further notes, references and trends, putting the work in modern context and highlighting potential directions for further research. Additionally, the book contains an entirely new chapter on contextual concept logic, including a section on description logics and relational concept analysis. As such, it should be a valuable resource for students, instructors and researchers at the crossroads of subject areas like Applied and Discrete Mathematics, Logics, Theoretical Computer Science, Knowledge Processing, Data Science, and is meant to be used both for research and in class, as a teaching resource. .
Author: Lingfei Wu
Publisher: Springer Nature
ISBN: 9811660549
Category : Computers
Languages : en
Pages : 701
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Book Description
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Author: Peter Eklund
Publisher: Springer Science & Business Media
ISBN: 3540210431
Category : Computers
Languages : en
Pages : 420
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Book Description
This book constitutes the refereed proceedings of the Second International Conference on Formal Concept Analysis, ICFCA 2004, held in Sydney, Australia in February 2004. The 27 revised full papers presented together with 7 invited papers were carefully reviewed and selected for inclusion in the book. Formal concept analysis emerged out of efforts to restructure lattice theory and has been extended into attribute exploration, Boolean judgment, and contextual logics in order to create a powerful general framework for knowledge representation and formal reasoning; among the application areas of formal concept analysis are data and knowledge processing, data visualization, information retrieval, machine learning, data analysis, and knowledge management. The papers in this book address all current issues in formal concept analysis, ranging from foundational and methodological issues to applications in various fields.
Author: Paul Kantor
Publisher: Springer Science & Business Media
ISBN: 3540259996
Category : Business & Economics
Languages : en
Pages : 692
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Book Description
This book constitutes the refereed proceedings of the IEEE International Conference on Intelligence and Security Informatics, ISI 2005, held in Atlanta, GA, USA in May 2005. The 28 revised full papers, 34 revised short papers, and 32 poster abstracts presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on data and text mining, infrastructure protection and emergency response, information management and security education, deception detection and authorship analysis, monitoring and surveillance, and terrorism informatics.
Author: Ripon Patgiri
Publisher: Springer
ISBN: 9789813347878
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings.
