Mining multivariate associations within GIS environments
Lee, Ickjai (2004) Mining multivariate associations within GIS environments. Lecture Notes in Artificial Intelligence, 3029 . pp. 1062-1071.
|PDF (Published Version) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
View at Publisher Website: http://dx.doi.org/10.1007/b97304
As geospatial data grows explosively, needs for the incorporation of data mining techniques into Geographic Information Systems (GISs) are in great demand. Association rules mining is a core technique in data mining and is a solid candidate for the cause-effect analysis of large geospatial databases. It efficiently detects frequent asymmetric causal patterns in large databases. In this paper, we investigate a series of geospatial preprocessing steps involving data conversion and classification so that traditional boolean and quantitative association rules mining can be applied. We present a robust geospatial multivariate association rules mining framework for efficient knowledge discovery within data-rich GISs environments. The proposed approach can be integrated into traditional GISs using dynamic link library and scripting languages such as AVENUE for ArcView and MapBasic for MapInfo. Our framework is designed and implemented in AVENUE for ArcView GIS. Experiments with real datasets demonstrate the robustness and efficiency of our approach.
|Item Type:||Article (Refereed Research - C1)|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080606 Global Information Systems @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%|
|Deposited On:||12 Mar 2010 14:37|
|Last Modified:||23 May 2013 00:59|
Last 12 Months: 0
|Citation Counts with External Providers:||Web of Science: 0|
Repository Staff Only: item control page