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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/72863
Title: 
Spatial clustering applied to health area
Author(s): 
Institution: 
Universidade Estadual Paulista (UNESP)
Abstract: 
The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.
Issue Date: 
1-Dec-2011
Citation: 
Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, p. 427-432.
Time Duration: 
427-432
Keywords: 
  • Database
  • Geographic information system
  • Spatial clustering
  • Spatial data mining
  • Work accidents
  • Geographic information
  • Distributed computer systems
  • Hardware
  • Geographic information systems
Source: 
http://dx.doi.org/10.1109/PDCAT.2011.76
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/72863
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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