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DAWAK
2010
Springer

Mining Closed Itemsets in Data Stream Using Formal Concept Analysis

9 years 10 days 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 stream environment because of the unbounded nature and no-second-look characteristics. In this paper, we propose an algorithm, CLICI, for mining all recent closed itemsets in landmark window model of online data stream. The algorithm consists of an online component, which processes the transactions arriving in the stream without candidate generation and updates the synopsis appropriately. The offline component is invoked on demand to mine all frequent closed itemsets. User can explore and experiment by specifying the support threshold dynamically. The synopsis, CILattice, stores all recent closed itemsets in the stream. It is based on Concept Lattice - a core structure of Formal Concept Analysis (FCA). Closed itemsets stored in the form of lattice facilitate generation of non-redundant association rules and ...
Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where DAWAK
Authors Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
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