SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.


Autoria(s): Poulose Jacob,K; Bindiya, Varghese M; Unnikrishnan, A
Data(s)

13/06/2014

13/06/2014

01/01/2013

Resumo

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Asian Journal of Computer Science And Information Technology 3: 1 (2013) 1 - 8.

Cochin University Of Science And Technology

Identificador

2249-5126

http://dyuthi.cusat.ac.in/purl/3901

Idioma(s)

en

Publicador

Asian Journal of Computer Science & Information Technology

Palavras-Chave #spatial clustering algorithms #two-dimensional abstraction #implicit relations of spatial neighborhood #spatial trend analysis
Tipo

Article