Comments on 'Parallel Algorithms for Hierarchical Clustering and Cluster Validity'
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Finding Boundary Shape Matching Relationships in Spatial Data
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A progressive refinement approach to spatial data mining
A progressive refinement approach to spatial data mining
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Reasoning about categories in conceptual spaces
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fast qualitative reasoning about categories in conceptual spaces
Design and application of hybrid intelligent systems
AN ORDER-k VORONOI APPROACH TO GEOSPATIAL CONCEPT MANAGEMENT WITHIN CONCEPTUAL SPACES
Applied Artificial Intelligence
Data mining coupled conceptual spaces for intelligent agents in data-rich environments
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Mining positive associations of urban criminal activities using hierarchical crime hot spots
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
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Spatial clustering provides answers for "where?" and "when?" and evokes "why?" for further explorations. In this paper, we propose a divisive multi-level clustering method that requires O(n log n) time. It reveals a cluster hierarchy for the "where?" and "when?" queries. Experimental results demonstrate that it identifies quality multi-level clusters. In addition, we present a solid framework for reasoning about multi-level clusters using Region Connection Calculus for the "why?" query. In this framework, we can derive their possible causes and positive associations between them with ease.