Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The design and analysis of spatial data structures
The design and analysis of spatial data structures
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
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 application of association rule mining to remotely sensed data
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Finding Interesting Associations without Support Pruning
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
k-nearest Neighbor Classification on Spatial Data Streams Using P-trees
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Association Rule Mining on Remotely Sensed Images Using P-trees
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
The traditional task of association rule mining is to find all rules with high support and high confidence. In some applications, such as mining spatial datasets for natural resource location, the task is to find high confidence rules even though the support may be low. In still other applications, such as the identification of agricultural pest infestations, the task is to find high confidence rules preferably while the support is still very low. The basic Apriori algorithm cannot be used to solve these problems efficiently since it relies on first identifying all high support itemsets. In this paper, we propose a new model to derive high confidence rules for spatial data regardless of their support level. A new data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. P-trees represent spatial data bit-by-bit in a recursive quadrant-by-quadrant arrangement. Based on the P-tree, we build a special data cube, the Tuple Count Cube (T-cube), to derive high confidence rules. Our algorithm for deriving confident rules is fast and efficient. In addition, we discuss some strategies for avoiding over-fitting (removing redundant and misleading rules).