Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ascending Frequency Ordered Prefix-tree: Efficient Mining of Frequent Patterns
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
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
Efficient closed pattern mining in the presence of tough block constraints
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
Index Support for Frequent Itemset Mining in a Relational DBMS
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining complex power networks for blackout prevention
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Improved methods for extracting frequent itemsets from interim-support trees
Software—Practice & Experience
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
Efficient mining of large maximal bicliques
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
A performance study of three disk-based structures for indexing and querying frequent itemsets
Proceedings of the VLDB Endowment
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Extensive efforts have been devoted to developing efficient algorithms for mining frequent patterns. However, frequent pattern mining remains a time-consuming process, especially for very large datasets. It is therefore desirable to adopt a "mining once and using many times" strategy. Unfortunately, there has been little work reported on managing and organizing a large set of patterns for future use. In this paper, we propose a disk-based data structure, CFP-tree (Condensed Frequent Pattern Tree), for organizing frequent patterns discovered from transactional databases. In addition to an efficient algorithm for CFP-tree construction, we also developed algorithms to efficiently support two important types of queries, namely queries with minimum support constraints and queries with item constraints, against the stored patterns, as these two types of queries are basic building blocks for complex frequent pattern related mining tasks. Comprehensive experimental study has been conducted to demonstrate the effectiveness of CFP-tree and efficiency of related algorithms.