Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining Closed Relational Graphs with Connectivity Constraints
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Cache-conscious frequent pattern mining on a modern processor
VLDB '05 Proceedings of the 31st international conference on Very large data bases
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Adaptive Parallel Graph Mining for CMP Architectures
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Parallel Mining of Frequent Closed Patterns: Harnessing Modern Computer Architectures
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
An integrated, generic approach to pattern mining: data mining template library
Data Mining and Knowledge Discovery
Mining Frequent Gradual Itemsets from Large Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Note: Listing closed sets of strongly accessible set systems with applications to data mining
Theoretical Computer Science
Mining tree-structured data on multicore systems
Proceedings of the VLDB Endowment
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
The iZi project: easy prototyping of interesting pattern mining algorithms
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Mining closed gradual patterns
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Itemset mining: A constraint programming perspective
Artificial Intelligence
An efficient framework for mining flexible constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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In this paper, we present ParaMiner which is a generic and parallel algorithm for closed pattern mining. ParaMiner is built on the principles of pattern enumeration in strongly accessible set systems. Its efficiency is due to a novel dataset reduction technique (that we call EL-reduction), combined with novel technique for performing dataset reduction in a parallel execution on a multi-core architecture. We illustrate ParaMiner's genericity by using this algorithm to solve three different pattern mining problems: the frequent itemset mining problem, the mining frequent connected relational graphs problem and the mining gradual itemsets problem. In this paper, we prove the soundness and the completeness of ParaMiner. Furthermore, our experiments show that despite being a generic algorithm, ParaMiner can compete with specialized state of the art algorithms designed for the pattern mining problems mentioned above. Besides, for the particular problem of gradual itemset mining, ParaMiner outperforms the state of the art algorithm by two orders of magnitude.