An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth international conference on Information and knowledge management
Efficient Algorithms for Incremental Update of Frequent Sequences
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences
Data Mining and Knowledge Discovery
Maintenance of generalized association rules with multiple minimum supports
Intelligent Data Analysis
Maintenance of discovered sequential patterns for record deletion
Intelligent Data Analysis
A new incremental data mining algorithm using pre-large itemsets
Intelligent Data Analysis
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
Maintenance of generalized association rules for record deletion based on the pre-large concept
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Incremental maintenance of generalized association rules under taxonomy evolution
Journal of Information Science
An efficient algorithm for mining frequent closed itemsets in dynamic transaction databases
International Journal of Intelligent Systems Technologies and Applications
Updating generalized association rules with evolving taxonomies
Applied Intelligence
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Incremental Mining of Ontological Association Rules in Evolving Environments
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
An incremental mining algorithm for maintaining sequential patterns using pre-large sequences
Expert Systems with Applications: An International Journal
SPO-Tree: efficient single pass ordered incremental pattern mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
A new mining approach for uncertain databases using CUFP trees
Expert Systems with Applications: An International Journal
On-line association rules mining with dynamic support
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Maintenance of generalized association rules under transaction update and taxonomy evolution
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Efficient remining of generalized association rules under multiple minimum support refinement
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Incremental association mining based on maximal itemsets
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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The association rules represent an important class of knowledge that can be discovered from data warehouses. Current research efforts are focused on inventing efficient ways of discovering these rules from large databases. As databases grow, the discovered rules need to be verified and new rules need to be added to the knowledge base. Since mining afresh every time the database grows is inefficient, algorithms for incremental mining are being investigated. Their primary aim is to avoid or minimize scans of the older database by using the intermediate data constructed during the earlier mining. In this paper, we present one such algorithm. We make use of large and candidate itemsets and their counts in the older database, and scan the increment to find which rules continue to prevail and which ones fail in the merged database. We are also able to find new rules for the incremental and updated database. The algorithm is adaptive in nature, as it infers the nature of the increment and avoids altogether, if possible, multiple scans of the incremental database. Another salient feature is that it does not need multiple scans of the older database. We also indicate some results on its performance against synthetic data.