Abstract:
The technical progress in computerized data
acquisition and storage has resulted in the growth of vast
databases for Geographical Information Systems. This has led to
continuous increase and accumulation of huge amounts of the
computerized data that have far exceeded human ability to
completely interpret, analyze and use. In order to understand
and make full use of these data repositories, various techniques
have been put forward. However, these techniques are not fully
reliable as they are not as efficient or of high performance as is
expected. This thesis attempts to improve on the efficiency of
existing spatial data mining techniques to ensure more efficient
and high performance spatial data mining functionality in the
present framework and tools used for spatial data mining. This
will be done by integrating various techniques with available
technologies.
The focus of this project is on improving performance and
efficiency of spatial clustering, one of the commonly used spatial
data mining methods by integrating visualization into clustering
with an aim to provide an interactive, efficient and user-friendly
approach to this important process for GIS data.
