Geographic Data Mining and Knowledge Discovery, Second Edition (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Autor: Miller, Harvey J.
Rok: 2009
ISBN: 9781420073973
NKP-CNB: boa001-muni.000595094
OKCZID: 110252304

Citace (dle ČSN ISO 690):
MILLER, Harvey J., ed. a HAN, Jiawei, ed. Geographic data mining and knowledge discovery. 2nd ed. Boca Raton: CRC Press/Taylor & Francis, c2009. 458 s. Chapman & Hall/CRC data mining and knowledge discovery series.

Anotace

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.

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