Opening the black box: interactive hierarchical clustering for multivariate spatial patterns

Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods have so far been mainly focused on searching for patterns within the spatial dimensions (usually 2D or 3D space), while more general-purpose high-dimensional (multivariate) clustering methods have very limited power in recognizing spatial patterns that involve neighbors. Secondly, existing clustering methods tend to be `closed' and are not geared toward allowing the interaction needed to effectively support a human-led exploratory analysis. The contribution of the research includes three parts. (1) Develop an effective and efficient hierarchical spatial clustering method, which can generate a 1-D spatial cluster ordering that preserves all the hierarchical clusters. (2) Develop a density- and grid-based hierarchical subspace clustering method to effectively identify highdimensional clus...

• Type: Journal
• Year: 2002
• Where: GIS
• Authors: Diansheng Guo, Donna Peuquet, Mark Gahegan
• Keywords: Automated Reasoning, Clustering Method, GIS 2002, Spatial, Spatial Clustering Method