Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Online association rule mining
SIGMOD '99 Proceedings of the 1999 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
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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The association rule mining and its usages have thrown the lights to the different possibilities for the researchers. The importance of association rule mining is getting increased day by day due to the proliferation of internet as well as the fiercer competition in the business domain. The time required for generating frequent itemsets plays an important role in the online stream mining. Some algorithms are designed as considering only the time factor. The data streams typically arrive continuously in high speed in huge amount and changing distribution. Here in this paper our effort is to improve the performance of the online stream mining algorithm as by developing a new algorithm with a new data structure. The result shows that the new approach improved the performance in the association rule mining in its online environment. The implementation of this algorithm has been tested using the datasets from Frequent Itemset Mining(FIM) dataset repository.