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Friday, July 31, 2020 | History

3 edition of Quality aspects in spatial data mining found in the catalog.

Quality aspects in spatial data mining

Quality aspects in spatial data mining

  • 218 Want to read
  • 16 Currently reading

Published by Chapman & Hall/CRC in Boca Raton, FL .
Written in English

    Subjects:
  • Geographic information systems.,
  • Spatial analysis (Statistics)

  • Edition Notes

    Includes bibliographical references and index.

    Statementeditors, Alfred Stein, John Shi, Wietske Bijker.
    ContributionsStein, Alfred., Shi, John., Bijker, Wietske, 1965-
    Classifications
    LC ClassificationsG70.212 .Q35 2008
    The Physical Object
    Paginationp. cm.
    ID Numbers
    Open LibraryOL18893705M
    ISBN 109781420069266
    LC Control Number2008009919

    Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats. Here data mining can be taken as data and mining, data is something that holds some records of information and mining can be considered as digging deep information about using in terms of defining, What is Data Mining? Data mining is a process that is useful for the discovery of informative and analyzing the understanding of the aspects of different elements.

    DANS is an institute of KNAW and NWO. Driven by data. Go to page top Go back to contents Go back to site navigationCited by: Abstract. Mining regression models from spatial data is a fundamental task in Spatial Data Mining. We propose a method, namely Mrs-SMOTI, that takes advantage from a tight-integration with spatial databases and mines regression models in form of trees in order to partition the sample space. The method is characterized by three aspects.

    Topics covered, among others, include strategies for end user education, current spatial data standards and their importance, legal issues and liabilities in the ownership and use of spatial data, spatial metadata use within distributed databases, the Internet and Web-based solutions to database deployment, quality assurance and quality control. Data Quality Aspects in Data Mining, Data Linkage and Geocoding Peter Christen Department of Computer Science, Faculty of Engineering and Information Technology, Spatial mining (geographical data analysis) Stream mining Where access to the data is limited to once (e.g. network.


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Quality aspects in spatial data mining Download PDF EPUB FB2

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In this cohesive collection of peer-reviewed chapters, field authorities present the latest field advancements and cover such essential areas as 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 Format: Hardcover. Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data QualitySubstantial 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 impreCited by: Get this from a library. Quality aspects in spatial data mining. [Alfred Stein; Wenzhong Shi; Wietske Bijker;] -- 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. Quality Aspects in Spatial Data Mining book. Quality Aspects in Spatial Data Mining. DOI link for Quality Aspects in Spatial Data Mining.

Quality Aspects in Spatial Data Mining book. Edited By Alfred Stein, Wenzhong Shi, Wietske Bijker. Edition 1st Edition. First Published Data quality is a pillar in any GIS implementation and application as reliable data are indispensable to allow the user obtaining meaningful results.

Spatial Data quality can be categorized into Data completeness, Data Precision, Data accuracy and Data Consistency. Get this from a library. Quality aspects in spatial data mining. [Alfred Stein; John Shi; Wietske Bijker; CRC Press.;] -- Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data QualitySubstantial progress has been made toward developing effective techniques for spatial information processing in.

Jiawei Han, Jian Pei, in Data Mining (Third Edition), Mining Spatial Data. Spatial data mining discovers patterns and knowledge from spatial data.

Spatial data, in many cases, refer to geospace-related data stored in geospatial data repositories. The data can be in “vector” or “raster” formats, or in the form of imagery and geo-referenced multimedia.

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 multi-dimensional data integration, multi-scale data representation, national or regional spatial data products, and new spatial data mining methods.

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 multi-scale data integration to uncertainties in spatial data mining, this book launches into 5/5(1).

The spatial data mining (SDM) method is a discovery process of extracting gener- alized knowledge from massive spatial data, which b uilds a pyramid from attribute space and feature space to. Spatial Data Mining Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography, meteorology, etc.

The main difference: spatial autocorrelation the neighbors of a spatial object may have an influence on it and therefore have to be considered as well Spatial attributes TopologicalFile Size: 1MB. Spatial Data Mining Page 4 / 30 Dec. 19, Elmar Witte, [email protected] Features of Spatial Data Structures (1) Introduction: Spatial Data Structures • spatial (ger.

räumlich) data mining means discovery of knowledge in spatial databases (similar but not identic to relational data mining) • spatial databases store (hugh amounts) of. Shekhar S., Xiong H. () Spatial Data Quality. In: Shekhar S., Xiong H. (eds) Encyclopedia of GIS. Spatial Aspects of Crime. Spatial Association.

Spatial Association Analysis Spatial Data Indexing with Uncertainty. Spatial Data Mining. Spatial Data Quality. Spatial Data Transfer Standard (SDTS) Over 10 million scientific documents at. Data quality refers to the state of qualitative or quantitative pieces of information.

There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. In book: Quality Aspects in Spatial Data Mining, Publisher: CRC press, Taylor and Francois Group, Editors: Alfred Stein, Wenzhong Shi and Wietske Bijker, pp Cite this publication Mir Author: Mir Abolfazl Mostafavi.

Spatial Data Mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of Cited by: More emphasis needs to be placed on the advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks.

This comprehensive data mining book explores the different aspects of data mining, starting from the fundamentals, and subsequently explores the complex data types and their applications. Quality aspects in spatial data mining.

By A Stain, WZ Shi and W Bijker. Abstract. Department of Land Surveying and Geo-Informatics > Academic research: not refereed > Edited book (editor Topics: Geographic information systems, Author: A Stain, WZ Shi and W Bijker.

Yun Yang, in Temporal Data Mining Via Unsupervised Ensemble Learning, Overview of Book. This book is enlightening for students and researchers wishing to study on temporal data mining and unsupervised ensemble learning approaches.

It is not only to enumerate the existing techniques proposed so far but also to classify and organize them in a way that may be of help for a practitioner.Quality Aspects in Spatial Data Mining by Alfred Stein, Wenzhong Shi, Wietske Bijker English | | ISBN: | pages | PDF | MB Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data Quality.

4 Introduction • Spatial data mining is the process of discovering interesting, useful, non-trivial patterns from large spatial datasets – E.g.

Determining hotspots: unusual locations. • Spatial Data Mining Tasks – Characteristics rule. – Discriminate rule. E.g. Comparison of price ranges of different geographical area.