Abstract
Geospatial clustering is an important topic in spatial analysis and knowledge discovery research. However, most existing clustering methods clusters geospatial data at data level without considering domain knowledge and users’ goals during the clustering process. In this paper, we propose an ontology-based geospatial cluster ensemble approach to produce better clustering results with the consideration of domain knowledge and users’ goals. The approach includes two components: an ontology-based expert system and a cluster ensemble method. The ontology-based expert system is to represent geospatial and clustering domain knowledge and to identify the appropriate clustering components (e.g., geospatial datasets, attributes of the datasets and clustering methods) based on a specific application requirement. The cluster ensemble is to combine a diverse set of clustering results which is produced by recommended clustering components into an optimal clustering result. A real case study has been conducted to demonstrate the efficiency and practicality of the approach.
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Notes
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Mutual information is a symmetric measure to quality the statistical information shared between two distributions.
- 2.
The number of current cancer care facilities in Alberta is 26.
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Wang, X., Gu, W. (2017). Using Ontology and Cluster Ensembles for Geospatial Clustering Analysis. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_35
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