Abstract
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 is the main motivation behind using lattice based synopsis.
Experimental evaluation using synthetic and real life datasets demonstrates the scalablility of the algorithm.
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Gupta, A., Bhatnagar, V., Kumar, N. (2010). Mining Closed Itemsets in Data Stream Using Formal Concept Analysis. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2010. Lecture Notes in Computer Science, vol 6263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15105-7_23
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DOI: https://doi.org/10.1007/978-3-642-15105-7_23
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