Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Mining Incremental Association Rules with Generalized FP-Tree
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
The fuzzy frequent pattern Tree
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Dependencies among attributes given by fuzzy confirmation measures
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Due to the increasing occurrence of very large databases, mining useful information and knowledge from transactions is evolving into an important research area. In the past, many algorithms were proposed for mining association rules, most of which were based on items with binary values. Transactions with quantitative values are, however, commonly seen in real-world applications. In this paper, the frequent fuzzy pattern tree (fuzzy FP-tree) is proposed for extracting frequent fuzzy itemsets from the transactions with quantitative values. When extending the FP-tree to handle fuzzy data, the processing becomes much more complex than the original since fuzzy intersection in each transaction has to be handled. The fuzzy FP-tree construction algorithm is thus designed, and the mining process based on the tree is presented. Experimental results on three different numbers of fuzzy regions also show the performance of the proposed approach.