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|Title:||Self-organizing neural networks for spatial data|
|Source:||Babu, G.P. (1997-02). Self-organizing neural networks for spatial data. Pattern Recognition Letters 18 (2) : 133-142. ScholarBank@NUS Repository.|
|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. © 1997 Elsevier Science B.V.|
|Source Title:||Pattern Recognition Letters|
|Appears in Collections:||Staff Publications|
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