Quality Aspects in Spatial Data Mining

Author: Alfred Stein
Publisher: CRC Press
ISBN: 1420069276
Format: PDF, ePub, Docs
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Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data Quality Substantial progress has been made toward developing effective techniques for spatial information processing in recent years. This science deals with models of reality in a GIS, however, and not with reality itself. Therefore, spatial information processes are often imprecise, allowing for much interpretation of abstract figures and data. Quality Aspects in Spatial Data Mining introduces practical and theoretical solutions for making sense of the often chaotic and overwhelming amount of concrete data available to researchers. In this cohesive collection of peer-reviewed chapters, field authorities present the latest field advancements and cover such essential areas as data acquisition, geoinformation theory, spatial statistics, and dissemination. Each chapter debuts with an editorial preview of each topic from a conceptual, applied, and methodological point of view, making it easier for researchers to judge which information is most beneficial to their work. Chapters Evolve From Error Propagation and Spatial Statistics to Address Relevant Applications The book advises the use of granular computing as a means of circumventing spatial complexities. This counter-application to traditional computing allows for the calculation of imprecise probabilities – the kind of information that the spatial information systems community wrestles with much of the time. Under the editorial guidance of internationally respected geoinformatics experts, this indispensable volume addresses quality aspects in the entire spatial data mining process, from data acquisition to end user. It also alleviates what is often field researchers’ most daunting task by organizing the wealth of concrete spatial data available into one convenient source, thereby advancing the frontiers of spatial information systems.

Data Mining Southeast Asia Edition

Author: Jiawei Han
Publisher: Elsevier
ISBN: 9780080475585
Format: PDF, ePub, Mobi
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Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site

Spatial Data Mining

Author: Deren Li
Publisher: Springer
ISBN: 3662485389
Format: PDF, Mobi
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· This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.

Anwendung des Spatial Data Mining SDM in der Epidemie Forschung

Author: Thomas Hillen
Publisher: GRIN Verlag
ISBN: 3656677506
Format: PDF
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Studienarbeit aus dem Jahr 2012 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 2,1, Fachhochschule Bielefeld, Veranstaltung: Seminar WINF, Sprache: Deutsch, Abstract: Die vorliegende Seminararbeit befasst sich mit dem Thema „Spatial Data-Mining und Epidemien“ (SDM). Das SDM ist eine Ausprägungsart des Data-Mining (DM). Es verknüpft räumliche Daten mit dem herkömmlichen Prozess der Wissensgewinnung (DM) aus Datenbanken. Das SDM ist hierbei lediglich ein Teilprozess der Wissensentdeckung in Datenbanken. Daher wird im Rahmen dieser Arbeit das Data-Mining an sich erläutert. Es wird ein Überblick über Geographische Informationssysteme gegeben und das „Spatial Data-Mining“, die dabei verwendeten Techniken sowie die betreffenden Anwendungsgebiete behandelt. Ein Anwendungsgebiet des SDM – die Epidemie-Forschung – wird detaillierter betrachtet und abschließend ein Fazit gezogen werden.

Spatial Database Systems

Author: Albert K.W. Yeung
Publisher: Springer Science & Business Media
ISBN: 9781402053924
Format: PDF, Mobi
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This book places spatial data within the broader domain of information technology (IT) while providing a comprehensive and coherent explanation of the guiding principles, methods, implementation and operational management of spatial databases within the workplace. The text explains the key concepts, issues and processes of spatial data implementation and provides a holistic management perspective.

Data Mining and Knowledge Discovery Handbook

Author: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 0387098232
Format: PDF, Kindle
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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.

Geographic Data Mining and Knowledge Discovery Second Edition

Author: Harvey J. Miller
Publisher: CRC Press
ISBN: 9781420073980
Format: PDF, Mobi
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The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field. New to the Second Edition Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques New chapter on data quality issues in GKD New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases New chapter that discusses the use of geographically weighted regression as an exploratory technique New chapter that gives an integrated approach to multivariate analysis and geovisualization Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.

Assessment of Spatial Data Mining Tools for Integration Into an Object Oriented GIS GIDB

Author:
Publisher:
ISBN:
Format: PDF, Docs
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A variety of data mining techniques are under evaluation on the spatial data of concern in our setting. We are planning to integrate a number of these techniques into our geospatial system (GIDB). Three approaches are under special consideration and are described in the paper. A COTS data mining system has been successfully used to develop predictive models of near-shore conditions such as wave height for naval amphibious operations. Attribute generalization was applied to seafloor data to obtain statements about conditions relevant to mine warfare. Finally an extension of association rule discovery applied to fuzzy spatial data that is under development is discussed.

Database Support for Data Mining Applications

Author: Rosa Meo
Publisher: Springer Science & Business Media
ISBN: 9783540224792
Format: PDF
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Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.