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
Data mining: concepts and techniques
Data mining: concepts and techniques
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Mining association rules using inverted hashing and pruning
Information Processing Letters
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information and Computation
An efficient cluster and decomposition algorithm for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
An approach to discovering multi-temporal patterns and its application to financial databases
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
A fast algorithm for frequent itemset mining using Patricia* structures
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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This paper proposes an efficient method, the frequent items ultrametric trees (FIUT), for mining frequent itemsets in a database. FIUT uses a special frequent items ultrametric tree (FIU-tree) structure to enhance its efficiency in obtaining frequent itemsets. Compared to related work, FIUT has four major advantages. First, it minimizes I/O overhead by scanning the database only twice. Second, the FIU-tree is an improved way to partition a database, which results from clustering transactions, and significantly reduces the search space. Third, only frequent items in each transaction are inserted as nodes into the FIU-tree for compressed storage. Finally, all frequent itemsets are generated by checking the leaves of each FIU-tree, without traversing the tree recursively, which significantly reduces computing time. FIUT was compared with FP-growth, a well-known and widely used algorithm, and the simulation results showed that the FIUT outperforms the FP-growth. In addition, further extensions of this approach and their implications are discussed.