A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Constraint-based clustering in large databases
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Clustering Spatial Data when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Density-based spatial clustering in the presence of obstacles and facilitators
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Utilisation of administrative registers using scientific knowledge discovery
Intelligent Data Analysis
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
An Ontology-Based Spatial Clustering Selection System
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
An ontology-based traffic accident risk mapping framework
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Hi-index | 0.00 |
Spatial clustering is an important topic in knowledge discovery research However, most clustering methods do not consider semantic information during the clustering process In this paper, we present ONTO_CLUST, a framework for ontology-based spatial clustering Using the framework, spatial clustering can be conducted with the support of a spatial clustering ontology As an illustration, the framework is applied to the problem of clustering Canadian population data.