Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Applying on-line bitmap indexing to reduce counting costs in mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
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
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
International Journal of Computer Applications in Technology
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Cosine interesting pattern discovery
Information Sciences: an International Journal
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Information Sciences: an International Journal
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Pattern mining of cloned codes in software systems
Information Sciences: an International Journal
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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A hyperclique pattern is a new type of association pattern that contains items which are highly affiliated with each other. Specifically, the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern. In this paper, we present an algorithm for mining maximal hyperclique patterns, which specifies a more compact representation of hyperclique patterns and are desirable for many applications, such as pattern-based clustering. Our algorithm exploits key advantages of both the Depth First Search (DFS) strategy and the Breadth First Search (BFS) strategy. Indeed, we adapt the equivalence pruning method, one of the most efficient pruning methods of the DFS strategy, into the process of the BFS strategy. Our experimental results show that the performance of our algorithm can be orders of magnitude faster than standard maximal frequent pattern mining algorithms, particularly at low levels of support.