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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth 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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The Item-Set Tree: A Data Structure for Data Mining
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Maintenance of maximal frequent itemsets in large databases
Proceedings of the 2007 ACM symposium on Applied computing
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
TARtool: A Temporal Dataset Generator for Market Basket Analysis
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Efficient frequent sequence mining by a dynamic strategy switching algorithm
The VLDB Journal — The International Journal on Very Large Data Bases
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Online structural graph clustering using frequent subgraph mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: 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
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This paper introduces an approach for incremental maximal frequent pattern (MFP) mining in sparse binary data, where instances are observed one by one. For this purpose, we propose the Augmented Itemset Tree (AIST), a data structure that incorporates features of the FP-tree into the itemset tree. In the given setting, we assume that just the data structure is maintained in main memory, and each instance is observed only once. The AIST not only stores observed frequent patterns, but also allows for quick frequency updates of relevant subpatterns. In order to quickly identify the current set of exact MFPs, potential candidates are extracted from former MFPs and patterns that occur in the new instance. The presented approach is evaluated concerning the runtime and memory requirements depending on the number of instances, minimum support and different settings of pattern properties. The obtained results suggest that AISTs are useful for mining maximal frequent itemsets in an online setting whenever larger patterns can be expected.