Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 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
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
Mining top-K frequent itemsets from data streams
Data Mining and Knowledge Discovery
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Interactive mining of frequent itemsets over arbitrary time intervals in a data stream
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
MMFI: An Effective Algorithm for Mining Maximal Frequent Itemsets
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
estMax: Tracing Maximal Frequent Itemsets over Online Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
Knowledge and Information Systems
Interactive Mining of Frequent Patterns in a Data Stream of Time-Fading Models
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
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
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
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Stream data mining is to extract useful patterns or knowledge from continuous, rapid data elements in modern applications. The discovery of frequent patterns in data streams generally is constrained by the usage of bounded memory and computation time. Most algorithms for mining frequent itemsets in streaming transactions assume a fixed minimum threshold and an unchangeable time interval. The support threshold, however, should be changeable to cope with the needs of the users and the characteristics of the incoming data. In addition, allowing the specification of the interesting time period of data may enhance the discovered knowledge. Still, the number of frequent itemsets might be too large to discovering the trends or changes. Thus, maximal frequent itemsets (MFIs) with respect to a changeable support in a user specified period become a favorable objective in stream data mining. In this paper, we propose an algorithm named VIMFI for mining MFIs in a data stream, allowing an arbitrary time interval and support threshold. A bounded memory space is allocated for summarizing all the transactions. VIMFI appends transactions to the summary structure and compresses the structure when it becomes full. Corresponding transactions in the specified interval will be extracted and a mining will be performed for the desired MFIs within that interval. Experiments using both synthetic and real-world datasets demonstrate that VIMFI efficiently mines MFIs in data streams with flexible time intervals and changeable support thresholds.