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
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
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Geovisual analytics for spatial decision support: Setting the research agenda
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Spatial hierarchies and topological relationships in the spatial MultiDimER model
BNCOD'05 Proceedings of the 22nd British National conference on Databases: enterprise, Skills and Innovation
GeWOlap: a web based spatial OLAP proposal
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
Spatial Clustering in SOLAP Systems to Enhance Map Visualization
International Journal of Data Warehousing and Mining
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The main purpose of SOLAP concept was to take advantage of the map visualization improving the analysis of data and enhancing the associated decision making process. However, in this environment, the map can easily become cluttered losing the benefits that triggered the appearance of this concept. In order to overcome this problem we propose a post-processing stage, which relies on a spatial clustering approach, to reduce the number of values to be visualized when this number is inadequate to a properly map analysis. The results obtained so far show that the usage of the post-processing stage is very useful to maintain a map suitable to the user's cognitive process. In addition, a novel heuristic to identify the threshold value from which the clusters must be generated was developed.