Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 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
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
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
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth 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
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
estMax: Tracing Maximal Frequent Itemsets over Online Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Approximately mining recently representative patterns on data streams
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
An approximate approach for mining recently frequent itemsets from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
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Stream data arrives dynamically and rapidly, and the characteristics cannot be reflected by the traditional transaction-based sliding window; thus, the mining results are inaccurate. This paper focuses on this problem and constructs a timestamp-based sliding window model, which can be further converted into a transaction-based sliding window. Based on this model, an extended enumeration tree is developed to incrementally maintain the essential information. In our proposed frequent itemset mining algorithm, we introduce the type transforming bound to dynamically classify the itemsets into categories; thus, certain itemset processing can be deferred or ignored, that is, an itemset will not be handled unless its type transforming bounds reach a threshold; as a result, the computational pruning can be conducted. Nevertheless, it only guarantees the conditions to obtain accurate results, and thus cannot achieve the best performance. This problem is further improved in our approximate mining algorithm, in which we propose a heuristic rule-based strategy. Additionally, it can save more computational cost with a tolerable mining error. Theoretical analysis and experimental studies demonstrate that our proposed algorithms have high accuracy, spend less computational time and memory, and significantly outperform the baseline method and state-of-the-art algorithms.