Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
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
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Quantitative evaluation of approximate frequent pattern mining algorithms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Maintenance of fast updated frequent pattern trees for record deletion
Computational Statistics & Data Analysis
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cartesian contour: a concise representation for a collection of frequent sets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards efficient mining of proportional fault-tolerant frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An improved frequent pattern growth method for mining association rules
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
An efficient algorithm for mining erasable itemsets
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
An efficient strategy for mining high utility itemsets
International Journal of Intelligent Information and Database Systems
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
Fast mining erasable itemsets using NC_sets
Expert Systems with Applications: An International Journal
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Knowledge and Information Systems
Engineering Applications of Artificial Intelligence
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules
Engineering Applications of Artificial Intelligence
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
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Erasable itemset (EI) mining is an interesting variation of frequent itemset mining which allows managers to carefully consider their production plans to ensure the stability of the factory. Existing algorithms for EI mining require a lot of time and memory. This paper proposes an effective algorithm, called mining erasable itemsets (MEI), which uses the divide-and-conquer strategy and the difference pidset (dPidset) concept for mining EIs fully. Some theorems for efficiently computing itemset information to reduce mining time up and memory usage are also derived. Experimental results show that MEI outperforms existing approaches in terms of both the mining time and memory usage. Moreover, the proposed algorithm is capable of mining EIs with higher thresholds than those obtained using existing approaches.