Self-organizing neural networks for spatial data

Abstract

In this paper, we present a self-organization neural network approach for spatial data visualization and spatial data indexing. Spatial data is typically used to represent multi-dimensional objects. Generally, for efficient processing such as indexing and retrieval, each multi-dimensional object is represented by an isothetic minimum bounding rectangle. Direct visualization of these multi-dimensional rectangles, denoting spatial objects, is not possible, if the number of dimensions exceeds three. Many linear and non-linear mapping techniques have been proposed in the literature for mapping point data, i.e., data that are points in multi-dimensional space. These approaches map points in higher-dimensional space to lower-dimensional space. Making use of these point data mapping approaches is a computationally intensive task as the number of points to be mapped is very large. In this paper, we propose a Kohonen's self-organization neural network approach for clustering spatial data. Cluster prototypes associated with nodes in the network are mapped into lower dimensions for data visualization using a non-linear mapping technique. We explain the applicability of this approach for efficient indexing of spatial data.