Fast detection of database system abuse behaviors based on data mining approach
Proceedings of the 2nd international conference on Scalable information systems
Mining frequent items in a stream using flexible windows
Intelligent Data Analysis - Knowledge Discovery from Data Streams
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Evaluating accuracies of a trading rule mining method based on temporal pattern extraction
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
A new algorithm for mining global frequent itemsets in a stream
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Mining top-K frequent itemsets through progressive sampling
Data Mining and Knowledge Discovery
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
Efficient term cloud generation for streaming web content
ICWE'10 Proceedings of the 10th international conference on Web engineering
Mining informative rule set for prediction over a sliding window
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A sliding window-based false-negative approach for ubiquitous data stream analysis
International Journal of Communication Systems
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
Mining frequent items in data stream using time fading model
Information Sciences: an International Journal
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
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Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. We also propose an algorithm based on the Lossy Counting Algorithm. In most of the experiments of the two proposed algorithms, we obtain perfect solutions and the memory space occupied by our algorithms is very small. Besides, we also propose the adapted approach of these two algorithms in order to handle the case when we are interested in mining the data in a sliding window. The experiments show that the results are accurate.