Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 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
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
CFI-Stream: mining closed frequent itemsets in data streams
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
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Expert Systems with Applications: An International Journal
Mining frequent patterns from dynamic data streams with data load management
Journal of Systems and Software
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
| Hi-index | 12.05 |
Frequent-pattern discovery in data streams is more challenging than that in traditional databases since several requirements need to be additionally satisfied. For the sliding-window model of data streams, transactions both enter into and leave from the window at each sliding. In this paper, we propose an approximation method for mining frequent itemsets over the sliding window of a data stream. The proposed method could approximate itemsets' counts from the counts of their subsets instead of scanning the transactions for them. By noticing the more dynamic feature of sliding-window model, we have made an effort to devise a promising technique which enables the proposed method to approximate for itemsets adaptively. In addition, another technique which may adjust and correct the approximations is also designed. Empirical results have shown that the performance of proposed method is quite efficient and stable; moreover, the mining result from adaptive approximation (and approximation adjustment) achieves high accuracy.