Uncertainty Handling and Quality Assessment in Data Mining

Uncertainty Handling and Quality Assessment in Data Mining PDF

Author: Michalis Vazirgiannis

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 231

ISBN-13: 144710031X

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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.

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data PDF

Author: Charu C. Aggarwal

Publisher: Springer Science & Business Media

Published: 2010-07-08

Total Pages: 494

ISBN-13: 0387096906

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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.

Intelligence and Security Informatics

Intelligence and Security Informatics PDF

Author: Hsinchun Chen

Publisher: Springer

Published: 2010-06-03

Total Pages: 182

ISBN-13: 364213601X

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This book constitutes the proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics 2010, held in Hyderabad, India, in June 2010.

Uncertainty Modelling and Quality Control for Spatial Data

Uncertainty Modelling and Quality Control for Spatial Data PDF

Author: Wenzhong Shi

Publisher:

Published: 2019-10-29

Total Pages: 0

ISBN-13: 9780367377144

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Focused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties-such as data of questionable quality-in geographic information science (GIS) applications. By using original research, current advancement, and emerging developments in the field, the authors compile various aspects of spatial data quality control. From multidimensional and multiscale data integration to uncertainties in spatial data mining, this book launches into areas that are rarely addressed. Topics covered include, New developments of uncertainty modelling, quality control of spatial data, and related research issues in spatial analysis, Spatial statistical solutions in spatial data quality, Eliminating systematic error in the analytical results of GIS applications, A data quality perspective for GIS function workflow design, Data quality in multidimensional integration, Research challenges on data quality in the integration and analysis of data from multiple sources, A new approach for imprecision management in the qualitative data warehouse, A multi-dimensional quality assessment of photogrammetric and LiDAR datasets based on a vector approach, An analysis on the uncertainty of multiscale representation for street-block settlement, Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals in GIS, and investigates the uncertainty modelling and quality control in multidimensional data integration, multiscale data representation, national or regional spatial data products, and new spatial data mining methods. Book jacket.

MIPPR 2005

MIPPR 2005 PDF

Author: Jianya Gong

Publisher: SPIE-International Society for Optical Engineering

Published: 2005

Total Pages: 554

ISBN-13:

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Proceedings of SPIE present the original research papers presented at SPIE conferences and other high-quality conferences in the broad-ranging fields of optics and photonics. These books provide prompt access to the latest innovations in research and technology in their respective fields. Proceedings of SPIE are among the most cited references in patent literature.

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data PDF

Author: Charu C. Aggarwal

Publisher: Springer

Published: 2010-07-08

Total Pages: 494

ISBN-13: 9780387096902

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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.

Data and Information Quality

Data and Information Quality PDF

Author: Carlo Batini

Publisher: Springer

Published: 2016-03-23

Total Pages: 500

ISBN-13: 3319241060

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This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.

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