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JIIS
2008
133views more  JIIS 2008»
14 years 9 months ago
Maintaining frequent closed itemsets over a sliding window
In this paper, we study the incremental update of Frequent Closed Itemsets (FCIs) over a sliding window in a high-speed data stream. We propose the notion of semi-FCIs, which is to...
James Cheng, Yiping Ke, Wilfred Ng
DBA
2006
182views Database» more  DBA 2006»
14 years 11 months ago
Matrix Apriori: Speeding Up the Search for Frequent Patterns
This work discusses the problem of generating association rules from a set of transactions in a relational database, taking performance and accuracy of found results as the essent...
Judith Pavón, Sidney Viana, Santiago G&oacu...
ACSW
2004
14 years 11 months ago
Cost-Efficient Mining Techniques for Data Streams
A data stream is a continuous and high-speed flow of data items. High speed refers to the phenomenon that the data rate is high relative to the computational power. The increasing...
Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady...
ICDM
2007
IEEE
254views Data Mining» more  ICDM 2007»
15 years 3 months ago
Sampling for Sequential Pattern Mining: From Static Databases to Data Streams
Sequential pattern mining is an active field in the domain of knowledge discovery. Recently, with the constant progress in hardware technologies, real-world databases tend to gro...
Chedy Raïssi, Pascal Poncelet
DAWAK
2010
Springer
14 years 10 months ago
Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...
Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar