SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining

Abstract

In GIS or Geographic Information system technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. Displaying and visualizing such data items are important aspects. But no RDBMS software is loaded with displaying the spatial result over a MAP overlay or answer spatial queries like all the points within certain Neighborhood. In this project, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of three parts: A Graphical User Interface for inputs and outputs, a Data Mining Module that processes spatial data mining functionalities, a Data Storage Model that stores and manages spatial as well as non-spatial data by using a DBMS. In particular, the data mining module provides major spatial data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. SD-Miner has its own characteristics: (1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively. (2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions. (3) It inputs parameters for mining as a form of database tables to increase flexibility. Result shows that significantly reduced and precise data items are displayed through the result of this technique.