Advances in Distributed and Parallel Knowledge Discovery

Advances in Distributed and Parallel Knowledge Discovery

Author: Hillol Kargupta

Publisher: AAAI Press

Published: 2000

Total Pages: 504

ISBN-13:

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This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang


Advanced Methods for Knowledge Discovery from Complex Data

Advanced Methods for Knowledge Discovery from Complex Data

Author: Ujjwal Maulik

Publisher: Springer Science & Business Media

Published: 2006-05-06

Total Pages: 375

ISBN-13: 1846282845

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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 following chapters.


Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook

Author: Oded Maimon

Publisher: Springer Science & Business Media

Published: 2010-09-10

Total Pages: 1269

ISBN-13: 0387098232

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This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.


Handbook of Research on Geographic Information Systems Applications and Advancements

Handbook of Research on Geographic Information Systems Applications and Advancements

Author: Faiz, Sami

Publisher: IGI Global

Published: 2016-10-21

Total Pages: 675

ISBN-13: 1522509380

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The proper management of geographic data can provide assistance to a number of different sectors within society. As such, it is imperative to continue advancing research for spatial data analysis. The Handbook of Research on Geographic Information Systems Applications and Advancements presents a thorough overview of the latest developments in effective management techniques for collecting, processing, analyzing, and utilizing geographical data and information. Highlighting theoretical frameworks and relevant applications, this book is an ideal reference source for researchers, academics, professionals, and students actively involved in the field of geographic information systems.


Data Mining for Scientific and Engineering Applications

Data Mining for Scientific and Engineering Applications

Author: R.L. Grossman

Publisher: Springer Science & Business Media

Published: 2013-12-01

Total Pages: 608

ISBN-13: 1461517338

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Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.


Computational Science and Its Applications - ICCSA 2004

Computational Science and Its Applications - ICCSA 2004

Author: Antonio Laganà

Publisher: Springer Science & Business Media

Published: 2004-05-07

Total Pages: 1066

ISBN-13: 3540220607

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The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience,thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14–17, 2004.


Agent and Multi-Agent Systems: Technologies and Applications

Agent and Multi-Agent Systems: Technologies and Applications

Author: Geun Sik Jo

Publisher: Springer

Published: 2008-04-03

Total Pages: 928

ISBN-13: 3540785825

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Following from the very successful First KES Symposium on Agent and Multi-Agent Systems – Technologies and Applications (KES-AMSTA 2007), held in Wroclaw, Poland, 31 May–1 June 2007, the second event in the KES-AMSTA symposium series (KES-AMSTA 2008) was held in Incheon, Korea, March 26–28, 2008. The symposium was organized by the School of Computer and Information Engineering, Inha University, KES International and the KES Focus Group on Agent and Mul- agent Systems. The KES-AMSTA Symposium Series is a sub-series of the KES Conference Series. The aim of the symposium was to provide an international forum for scientific research into the technologies and applications of agent and multi-agent systems. Agent and multi-agent systems are related to the modern software which has long been recognized as a promising technology for constructing autonomous, complex and intelligent systems. A key development in the field of agent and multi-agent systems has been the specification of agent communication languages and formalization of ontologies. Agent communication languages are intended to provide standard declarative mechanisms for agents to communicate knowledge and make requests of each other, whereas ontologies are intended for conceptualization of the knowledge domain. The symposium attracted a very large number of scientists and practitioners who submitted their papers for nine main tracks concerning the methodology and applications of agent and multi-agent systems, a doctoral track and two special sessions.


Encyclopedia of Data Warehousing and Mining

Encyclopedia of Data Warehousing and Mining

Author: Wang, John

Publisher: IGI Global

Published: 2005-06-30

Total Pages: 1382

ISBN-13: 1591405599

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Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.


Data Mining Techniques in Grid Computing Environments

Data Mining Techniques in Grid Computing Environments

Author: Werner Dubitzky

Publisher: John Wiley & Sons

Published: 2008-10-13

Total Pages: 288

ISBN-13: 0470699892

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Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.


Intelligent Information Agents

Intelligent Information Agents

Author: Matthias Klusch

Publisher: Springer Science & Business Media

Published: 2003-03-05

Total Pages: 281

ISBN-13: 3540007598

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This book presents 10 chapters on various aspects of intelligent information agents contributed by members of the respective AgentLink special interest group. The papers are organized in three parts on agent-based information systems, adaptive information agents, and coordination of information agents. Also included are a comprehensive introduction and surveys for each of the three parts.