Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Issues in data stream management
ACM SIGMOD Record
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
TFP: An Efficient Algorithm for Mining Top-K 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
Research issues in data stream association rule mining
ACM SIGMOD Record
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
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
Knowledge and Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Efficient prime-based method for interactive mining of frequent patterns
Expert Systems with Applications: An International Journal
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
| Hi-index | 12.06 |
Mining closed frequent itemsets from data streams is of interest recently. However, it is not easy for users to determine a proper minimum support threshold. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, an interactive single-pass algorithm, called TKC-DS (top-K frequent closed itemsets of data streams), is proposed for mining top-K closed itemsets from data streams efficiently. A novel data structure, called CIL (closed itemset lattice), is developed for maintaining the essential information of closed itemsets generated so far. Experimental results show that the proposed TKC-DS algorithm is an efficient method for mining top-K frequent itemsets from data streams.