BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Distance-Associated Join Indices for Spatial Range Search
Proceedings of the Eighth 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
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
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The number and the size of spatial databases are rapidly growing in applications such as geomarketing, astrophysics, and molecular biology. This is mainly due to the amazing progress in scientific instruments such as satellites with remote sensors or X-ray crystallography. While a lot of algorithms have been developed for knowledge discovery in relational databases, the field of knowledge discovery in spatial databases has only recently emerged (see Koperski et al., 1996, for an overview). The assumption of independently and identically distributed attributes, which is implicit in classical data mining, may not be applicable for spatial data. Attributes of the neighbors of some object of interest may have an influence on the object itself. For instance, a new industrial plant may pollute its neighborhood depending on the distance and on the major direction of the wind. In Section 1, we introduce spatial database systems and some basic operations for mining in such databases. Then, we discuss the major data mining tasks of spatial clustering (Section 2), spatial classification (Section 3), and spatial characterization (Section 4).