Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
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Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules; they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. We have proposed a novel approach of mining spatial positive and negative association rules. The approach applies multiple level spatial mining methods to extract interesting patterns in spatial and/or non-spatial predicates. Data and spatial predicates/association-ship are organized as set hierarchies to mine them level-by-level as required for multilevel spatial positive and negative association rules. A pruning strategy is used in our approach to efficiently reduce the search space. Further efficiency is gained by interestingness measure.