Spatial Clustering Using Hierarchical SOM

  • Henriques R
  • Lobo V
  • Bacao F
N/ACitations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

The amount of available geospatial data increases every day, placing additional pressure on existing analysis tools. Most of these tools were developed for a data poor environment and thus rarely address concerns of efficiency, high-dimensionality and automatic exploration [1]. Recent technological innovations have dramatically increased the availability of data on location and spatial characterization, fostering the proliferation of huge geospatial databas‐ es. To make the most of this wealth of data we need powerful knowledge discovery tools, but we also need to consider the particular nature of geospatial data. This context has raised new research challenges and difficulties on the analysis of multidimensional geo-referenced data. The availability of methods able to perform “intelligent” data reduction on vast amounts of high dimensional data is a central issue in Geographic Information Science (GISc) current research agenda. The

Cite

CITATION STYLE

APA

Henriques, R., Lobo, V., & Bacao, F. (2012). Spatial Clustering Using Hierarchical SOM. In Applications of Self-Organizing Maps. InTech. https://doi.org/10.5772/51159

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free