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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
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
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Efficient Single-Pass Mining of Weighted Interesting Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Handling Dynamic Weights in Weighted Frequent Pattern Mining
IEICE - Transactions on Information and Systems
Frequent pattern mining using modified CP-tree for knowledge discovery
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Enumeration tree based emerging patterns mining by using two different supports
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a database with minimal re-computation when new transactions are added to or old transactions are removed from the transaction database. An efficient algorithm called EFPIM (Extending FP-tree for Incremental Mining), is designed based on EFP-tree (extended FP-tree) structures. An important feature of our algorithm is that it requires no scan of the original database, and the new EFP-tree structure of the updated database can be obtained directly from the EFP-tree of the original database. We give two versions of EFPIM algorithm, called EFPIM1 (an easy vision to implement) and EFPIM2 (a fast algorithm), they both mining frequent itemsets of the updated database based on EFP-tree. Experimental results show that EFPIM outperforms the existing algorithms in terms of the execution time.