Identifying Clusters in Marked Spatial Point Processes: A Summary of Results

Abstract

Clustering of marked spatial point process is an important problem in many application domains (e.g. Behavioral Ecology). Classical clustering approaches handle homogeneous spatial points and hence cannot cluster marked spatial point process. In this paper, we propose a novel intuitive approach, Merge Algorithm, to hierarchically cluster marked spatial point process. This approach treats all spatial point processes in a dendrogram's sub-tree as a single spatial point process while clustering. The resulting dendrogram for marked spatial point process needs be analyzed by a domain expert to identify clusters. To remove the subjective nature of the clusters identified, we propose a novel statistical method, Cluster Identification Algorithm, to partition a dendrogram into clusters. This approach identifies (cuts) a dendrogram's sub-tree as a cluster if that subtree's intra-subtree similarity is significantly higher than inter-subtree similarity. Experiments with Jane Goodall Institute's chimpanzee ecological dataset from the Gombe National Park, Tanzania which shows that our proposed methods identified clusters which were compatible to those identified by domain experts.