The rapidly expanding market for Spatial Data Mining systems and technologies is driven by pressure from the public sector, environmental agencies and industry to provide innovative solutions to a wide range of different problems. The main objective of the described spatial data mining platform is to provide an open, highly extensible, n-tier system architecture based on Java 2 Platform, Enterp...

association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

There is a well known necessity to extract knowledge from spatial databases. Dozens of algorithms for data mining and knowledge discovery are reported in the specific literature to supply this necessity. However, these algorithms have some general drawbacks. Some consider only spatial data and others, only non-spatial data. Most are pseudo-codes, which are usually not implemented in toolkits, a...

Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules; they have not considered the spat...

This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is the task of unfolding the implicit knowledge hidden in the spatial databases. The spatial Databases contain both spatial and non-spatial attributes of the a...

The increment of spatial-temporal data (STD) collected in many domain areas, including bioinformatics, engineering, medicine, environment, telecommunication, computer vision and many more has leads towards the emergent of information technology initiatives that facilitate the knowledge acquisition, organization and dissemination among research community. The initiatives include but not exhaust ...

In the Association rule mining, originally proposed form market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Association Rule mining is one of the most popular data mining techniques which can be defined as extracting the interesting correlation and relation among large volume of transac...