Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Future Generation Computer Systems - Special double issue on data mining
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
Exploratory spatio-temporal data mining and visualization
Journal of Visual Languages and Computing
Context-based market basket analysis in a multiple-store environment
Decision Support Systems
Fuzzy Association Rule Mining from Spatio-temporal Data
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
International Journal of Geographical Information Science
Journal of Intelligent Information Systems
A soft set approach for association rules mining
Knowledge-Based Systems
Data mining applications in hydrocarbon exploration
Artificial Intelligence Review
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
An FP-tree based approach for mining all strongly correlated item pairs
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A novel approach of multilevel positive and negative association rule mining for spatial databases
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
A context aware sound classifier applied to prawn feed monitoring and energy disaggregation
Knowledge-Based Systems
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This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.