Combining Spatial and Non-spatial Data for Knowledge Discovery
| AUTHOR | Dey, Shubhamoy |
| PUBLISHER | LAP Lambert Academic Publishing (08/17/2011) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
Show More
Product Format
Product Details
ISBN-13:
9783845418834
ISBN-10:
3845418834
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
296
Carton Quantity:
28
Product Dimensions:
6.00 x 0.67 x 9.00 inches
Weight:
0.96 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Computers | General
Descriptions, Reviews, Etc.
publisher marketing
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.
Show More
Your Price
$101.32
