Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Efficient Search of Reliable Exceptions
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Exception Instances to Facilitate Workflow Exception Handling
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Top 10 algorithms in data mining
Knowledge and Information Systems
Detecting small group activities from multimodal observations
Applied Intelligence
DRFP-tree: disk-resident frequent pattern tree
Applied Intelligence
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
Approximating the number of frequent sets in dense data
Knowledge and Information Systems
Data mining on multimedia data
Data mining on multimedia data
StatApriori: an efficient algorithm for searching statistically significant association rules
Knowledge and Information Systems
Interpreting PET scans by structured patient data: a data mining case study in dementia research
Knowledge and Information Systems
Anytime mining for multiuser applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
Mining non-redundant time-gap sequential patterns
Applied Intelligence
Hi-index | 0.00 |
Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising.