Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Online association rule mining
SIGMOD '99 Proceedings of the 1999 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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Issues in data stream management
ACM SIGMOD Record
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Interactive sequence discovery by incremental mining
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets
IEEE Transactions on Knowledge and Data Engineering
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Research issues in data stream association rule mining
ACM SIGMOD Record
A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Variable support mining of frequent itemsets over data streams using synopsis vectors
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches assume a fixed support threshold, a changeable threshold is more realistic, considering the rapid updating of the streaming transactions in practice. Additionally, mining of itemsets over various time granularities rather than over the entire stream may provide more flexibility for many applications. Therefore, we propose a interactive mechanism to perform the mining of frequent itemsets over arbitrary time intervals in the data stream, allowing a changeable support threshold. A synopsis vector having tilted-time tables is devised for maintaining statistics of past transactions for support computation over user-specified time periods. The extensive experiments over various parameter settings demonstrate that our approach is efficient and capable of mining frequent itemsets in the data stream interactively, with variable support thresholds.