Spatial clustering is an important tool for spatial data mining and spatial analysis. It can be
used to discover the spatial association rules and spatial outliers in spatial datasets …

In this paper, a hierarchical spatial clustering algorithm based on field theory (HSCBFT in
abbreviation) is proposed. The field theory of spatial data is firstly employed to describe the …

Spatial clustering under spatial and attribute constraints is the clustering analysis on the
spatial dataset with non-spatial attributes, which is named dual clustering. The result of dual …

Due to the inherent characteristics of spatial datasets, spatial clustering methods need to
consider spatial attributes, non-spatial attributes and spatial correlation among non-spatial …

Exploratory data analysis is increasingly more necessary as larger spatial data is managed
in electro-magnetic media. Spatial clustering is one of the very important spatial data mining …

A hybrid spatial clustering method based on graph theory and spatial density (HGDSC) is
developed. The HGDSC method employs Delaunay triangulation to model the spatial …

Geometrical properties and attributes are two important characteristics of a spatial object. In
previous spatial clustering studies, these two characteristics were often neglected. This …

Spatial clustering is the process of grouping similar objects based on their distance,
connectivity, or relative density in space. It has been employed in the field of spatial analysis …

As an important part of machine learning, clustering methods have been continuously paid
attention to. Current clustering methods divide data objects usually based on Euclidean …

Spatial clustering analysis is an important spatial data mining technique. It divides objects
into clusters according to their similarities in both location and attribute aspects. It plays an …