BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Computers & Geosciences - Special issue: neural network applications in the geosciences
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Techniques for Design and Implementation of Efficient Spatial Access Methods
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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The outgrowth of technology in geographical databases has enhanced the growth of spatial databases, to deal with such enlarging databases scientists are laying down enormous efforts that can efficiently process these databases. Spatial data mining techniques has been collaboratively applied to extract implicit knowledge from spatial as well as non-spatial attributes. These techniques are efficiently applied in several fields such as healthcare, environmental, marketing and remote sensing databases to improve planning and decision making process. In this paper, we have designed and implemented SpaGRID framework for detection of spatial clusters. The framework has unprecedented efficiency to extract implicit knowledge of spatial data, due to its accessibility to handle and discover hidden patterns from spatial databases. We have also illustrated the usage of spatial variations among the United States men with prevalence of prostate cancer disease. The data of age group was taken from (15-65+) years in this group prostate cancers were examined and several stages of disease diagnosis was taken into account. The population of data was characterized by white, black and others were too small to be taken into account. Numerous challenges were encountered due to complexity of spatial datasets hence being resolved by certain statistical measures. The approach is to discover knowledge from spatial databases and design different aspects of knowledge discovery process from spatial databases.