Greedy Optimization for Contiguity-Constrained Hierarchical Clustering

The discovery and construction of inherent regions in large spatial datasets is an important task for many research domains such as climate zoning, eco-region analysis, public health mapping, and political redistricting. From the perspective of cluster analysis, it requires that each cluster is geographically contiguous. This paper presents a contiguity constrained hierarchical clustering and optimization method that can partition a set of spatial objects into a hierarchy of contiguous regions while optimizing an objective function. The method consists of two steps: contiguity constrained hierarchical clustering and two-way fine-tuning. The above two steps are repeated to create a hierarchy of regions. Evaluations and comparison show that the proposed method consistently and significantly outperforms existing methods by a large margin in terms of optimizing the objective function. Moreover, the method is flexible to accommodate different objective functions and additional constraints (...

• Type: Journal
• Year: 2009
• Where: ICDM
• Authors: Diansheng Guo
• Keywords: Data Mining, Hierarchical Clustering, ICDM 2009, Large Spatial Datasets, Objective Functions