Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Mutually Dependent Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining complex matchings across Web query interfaces
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Discovering complex matchings across web query interfaces: a correlation mining approach
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Automatic complex schema matching across Web query interfaces: A correlation mining approach
ACM Transactions on Database Systems (TODS)
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-k strongly correlated item pairs without minimum correlation threshold
International Journal of Knowledge-based and Intelligent Engineering Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
Association Mining in Large Databases: A Re-examination of Its Measures
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
Association rules mining including weak-support modes using novel measures
WSEAS Transactions on Computers
Tight correlated item sets and their efficient discovery
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Mining correlated subgraphs in graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
Discovering itemset interactions
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Mining significant least association rules using fast SLP-growth algorithm
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Scaling up top-K cosine similarity search
Data & Knowledge Engineering
DS'10 Proceedings of the 13th international conference on Discovery science
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
Efficient mining of top correlated patterns based on null-invariant measures
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Cosine interesting pattern discovery
Information Sciences: an International Journal
Efficiently mining both association and correlation rules
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
An FP-tree based approach for mining all strongly correlated item pairs
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Mining both associated and correlated patterns
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Efficiently mining maximal frequent mutually associated patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
Efficient computation of measurements of correlated patterns in uncertain data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
Mining popular patterns from transactional databases
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Confirmation measures of association rule interestingness
Knowledge-Based Systems
Mining associated sensor patterns for data stream of wireless sensor networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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Association rule mining often generates a huge numberof rules, but a majority of them either are redundantor don not reflect the tue correlation relationship amongdata objects.In this paper, we re-examine this problemand show that two interesting measures, all_confidence(denoted as \alpha) and coherence (denoted as \gamma), both disclosegenuine correlation relationships and can be computedefficiently.Moreover, we propose two interestingalgorithms, CoMine(\alpha) and CoMine(\gamma), based onextensions of a pattern-growth methodology.Our performancestudy shows that the CoMine algorithms havehigh performance in comparison with their Apriori-basedcounterpart algorithms.