Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/111209
DC Field | Value | |
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dc.title | Self-organizing neural networks for spatial data | |
dc.contributor.author | Babu, G.P. | |
dc.date.accessioned | 2014-11-27T09:45:47Z | |
dc.date.available | 2014-11-27T09:45:47Z | |
dc.date.issued | 1997-02 | |
dc.identifier.citation | Babu, G.P. (1997-02). Self-organizing neural networks for spatial data. Pattern Recognition Letters 18 (2) : 133-142. ScholarBank@NUS Repository. | |
dc.identifier.issn | 01678655 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/111209 | |
dc.description.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. | |
dc.source | Scopus | |
dc.subject | Clustering | |
dc.subject | Indexing | |
dc.subject | Kohonen network | |
dc.subject | Spatial data | |
dc.type | Article | |
dc.contributor.department | INSTITUTE OF SYSTEMS SCIENCE | |
dc.description.sourcetitle | Pattern Recognition Letters | |
dc.description.volume | 18 | |
dc.description.issue | 2 | |
dc.description.page | 133-142 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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