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Combining Spatial and Non-spatial Data for Knowledge Discovery: Combined Mining of Spatial and Non-spatial Data - Softcover
Synopsis
This book is the result of innovative research in combining data from spatial and non-spatial sources for knowledge discovery. Though there has been an explosive growth in data collection and storage capability during the last two decades and many of the data repositories contain location data in the form of spatial references, the wealth of spatial information present in the data is seldom utilised. This unique work establishes that mining of GIS (or other forms of spatial) data in conjunction with the non-spatial data linked together by the location information can successfully detect broad spatial trends over large spatial extents, as well as localised spatial associations. The applicability of the novel framework and algorithmic solutions developed has been demonstrated using real sales data from a supermarket chain. The contents within these covers will be found useful both by data mining students and researchers, and practitioners who would like to use the solution developed for gaining business benefit.
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About the Author
Dr. Shubhamoy Dey is Associate Professor of Information Systems at Indian Institute of Management. He completed his Ph.D from School of Computing, University of Leeds, UK, and Master of Technology from Indian Institute of Technology (IIT-Kgp). He specializes in Data Mining and has 25 years of consulting and teaching experience in UK, USA and India.
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Combining Spatial and Non-spatial Data for Knowledge Discovery
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LAP LAMBERT Academic Publishing Aug 2011, 2011
ISBN 10: 3845418834
ISBN 13: 9783845418834
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is the result of innovative research in combining data from spatial and non-spatial sources for knowledge discovery. Though there has been an explosive growth in data collection and storage capability during the last two decades and many of the data repositories contain location data in the form of spatial references, the wealth of spatial information present in the data is seldom utilised. This unique work establishes that mining of GIS (or other forms of spatial) data in conjunction with the non-spatial data linked together by the location information can successfully detect broad spatial trends over large spatial extents, as well as localised spatial associations. The applicability of the novel framework and algorithmic solutions developed has been demonstrated using real sales data from a supermarket chain. The contents within these covers will be found useful both by data mining students and researchers, and practitioners who would like to use the solution developed for gaining business benefit. 296 pp. Englisch. Seller Inventory # 9783845418834
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Combining Spatial and Non-spatial Data for Knowledge Discovery
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ISBN 10: 3845418834
ISBN 13: 9783845418834
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Dey ShubhamoyDr. Shubhamoy Dey is Associate Professor of Information Systems at Indian Institute of Management. He completed his Ph.D from School of Computing, University of Leeds, UK, and Master of Technology from Indian Institute o. Seller Inventory # 5481553
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Combining Spatial and Non-spatial Data for Knowledge Discovery | Combined Mining of Spatial and Non-spatial Data
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ISBN 10: 3845418834
ISBN 13: 9783845418834
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Taschenbuch. Condition: Neu. Combining Spatial and Non-spatial Data for Knowledge Discovery | Combined Mining of Spatial and Non-spatial Data | Shubhamoy Dey | Taschenbuch | 296 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845418834 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106839837
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Combining Spatial and Non-spatial Data for Knowledge Discovery
Published by
LAP LAMBERT Academic Publishing Aug 2011, 2011
ISBN 10: 3845418834
ISBN 13: 9783845418834
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is the result of innovative research in combining data from spatial and non-spatial sources for knowledge discovery. Though there has been an explosive growth in data collection and storage capability during the last two decades and many of the data repositories contain location data in the form of spatial references, the wealth of spatial information present in the data is seldom utilised. This unique work establishes that mining of GIS (or other forms of spatial) data in conjunction with the non-spatial data linked together by the location information can successfully detect broad spatial trends over large spatial extents, as well as localised spatial associations. The applicability of the novel framework and algorithmic solutions developed has been demonstrated using real sales data from a supermarket chain. The contents within these covers will be found useful both by data mining students and researchers, and practitioners who would like to use the solution developed for gaining business benefit.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 296 pp. Englisch. Seller Inventory # 9783845418834
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Combining Spatial and Non-spatial Data for Knowledge Discovery : Combined Mining of Spatial and Non-spatial Data
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845418834
ISBN 13: 9783845418834
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is the result of innovative research in combining data from spatial and non-spatial sources for knowledge discovery. Though there has been an explosive growth in data collection and storage capability during the last two decades and many of the data repositories contain location data in the form of spatial references, the wealth of spatial information present in the data is seldom utilised. This unique work establishes that mining of GIS (or other forms of spatial) data in conjunction with the non-spatial data linked together by the location information can successfully detect broad spatial trends over large spatial extents, as well as localised spatial associations. The applicability of the novel framework and algorithmic solutions developed has been demonstrated using real sales data from a supermarket chain. The contents within these covers will be found useful both by data mining students and researchers, and practitioners who would like to use the solution developed for gaining business benefit. Seller Inventory # 9783845418834
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