ACM Transactions on Database Systems (TODS)
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
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
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the Seventh International Conference on 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
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Spatial Subgroup Discovery Applied to the Analysis of Vegetation Data
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Knowledge discovery in databases: the purpose, necessity, and challenges
Handbook of data mining and knowledge discovery
Data mining tasks and methods: spatial analysis
Handbook of data mining and knowledge discovery
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Extracting spatial association rules from spatial transactions
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Mining frequent geographic patterns with knowledge constraints
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Knowledge discovery from spatial transactions
Journal of Intelligent Information Systems
Detecting anomalous longitudinal associations through higher order mining
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
International Journal of Geographical Information Science
A First Study on Clustering Collections of Workflow Graphs
Provenance and Annotation of Data and Processes
Effective spatial clustering methods for optimal facility establishment
Intelligent Data Analysis
Journal of Intelligent Information Systems
Spatial operators for evolving dynamic Bayesian networks from spatio-temporal data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A niched genetic programming algorithm for classification rules discovery in geographic databases
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
AntTrend: stigmergetic discovery of spatial trends
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
View-Angle of spatial data mining
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Nature-Inspired approaches to mining trend patterns in spatial databases
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Data Mining Approaches for Geo-Spatial Big Data: Uncertainty Issues
International Journal of Organizational and Collective Intelligence
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Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. We show that typical spatial data mining algorithms are well supported by the proposed basic operations. For finding significant spatial patterns, only certain classes of paths “leading away” from a starting object are relevant. We discuss filters allowing only such neighborhood paths which will significantly reduce the search space for spatial data mining algorithms. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.