Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Itemsets from Secondary Memory
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Out-of-core frequent pattern mining on a commodity PC
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient Mining of Frequent Itemsets from Data Streams
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Mining of Frequent Itemsets from Streams of Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
A new class of constraints for constrained frequent pattern mining
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A General Method for Estimating Correlated Aggregates over a Data Stream
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Sketch-based querying of distributed sliding-window data streams
Proceedings of the VLDB Endowment
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Mining frequent itemsets from sparse data streams in limited memory environments
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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With advances in technology, streams of data are produced in many applications. Efficient techniques for extracting implicit, previously unknown, and potentially useful information (e.g., in the form frequent sets) from data streams are in demand. Many existing stream mining algorithms capture important streaming data and assume that the captured data can fit into main memory. However, problem arose when the available memory is so limited that such an assumption does not hold. In this paper, we propose a novel data structure called DSTable to capture important data from the streams onto the disk. The DSTable can be easily maintained; it can be applicable for mining frequent sets from datasets, especially in limited memory environments.