Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
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
Deriving High Confidence Rules from Spatial Data Using Peano Count Trees
WAIM '01 Proceedings of the Second International Conference on Advances in Web-Age Information Management
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
Discovering Association Rules Based on Image Content
ADL '99 Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
P-tree classification of yeast gene deletion data
ACM SIGKDD Explorations Newsletter
Parameter optimized, vertical, nearest-neighbor-vote and boundary-based classification
ACM SIGKDD Explorations Newsletter
Efficient mining of salinity and temperature association rules from ARGO data
Expert Systems with Applications: An International Journal
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Temporal-spatial association analysis of ocean salinity and temperature variations
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
A fast calculation of metric scores for learning Bayesian network
International Journal of Automation and Computing
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Association Rule Mining, originally proposed for market basket data, has potential applications in many areas. Remote Sensed Imagery (RSI) data is one of the promising application areas. Extracting interesting patterns and rules from datasets composed of images and associated ground data, can be of importance in precision agriculture, community planning, resource discovery and other areas. However, in most cases the image data sizes are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an approach to derive association rules on RSI data using Peano Count Tree (P-tree) structure. P-tree structure, proposed in our previous work, provides a lossless and compressed representation of image data. Based on P-trees, an efficient association rule mining algorithm P-ARM with fast support calculation and significant pruning techniques are introduced to improve the efficiency of the rule mining process. P-ARM algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.