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
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
Efficient frequent sequence mining by a dynamic strategy switching algorithm
The VLDB Journal — The International Journal on Very Large Data Bases
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
Efficient Mining of Closed Sequential Patterns on Stream Sliding Window
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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
Stream mining of frequent sets with limited memory
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Floods of data can be produced in many applications such as Web click streams or wireless sensor networks. Hence, algorithms for mining frequent itemsets 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 so limited that such an assumption does not hold. In this paper, we present a data structure called DSTable to capture important data from the streams onto the disk. The DSTable can be easily maintained and is applicable for mining frequent itemsets from streams (especially sparse data) in limited memory environments.