An effective hashtable-based approach for incrementally mining closed frequent itemsets using sliding windows

M. Jeya Sutha and F. Ramesh Dhanaseelan

International Journal of Data Mining, Modelling and Management, 2016, vol. 8, issue 4, 382-404

Abstract: Online mining of closed frequent itemsets over streaming data is one of the important problems in mining data streams. In this paper, we propose a new algorithm called 'CFI-StreamSW' (mining closed frequent itemsets over data streams using sliding window), for mining the set of closed frequent itemsets. An effective hash table based approach is followed where two tables are used; one for storing all the items in the transactions and another for closed frequent itemsets. Thus, it does not store any other intermediate nodes or even frequent nodes. Experiments show that the proposed algorithm runs faster and consume less memory than existing algorithms 'NewMoment' and 'MWFP-SW' for mining closed frequent itemsets over recent data streams.

Keywords: data mining; closed frequent itemsets; sliding windows; SWs; landmark windows; damped windows; data streams; incremental mining; hash table; stream mining; single pass mining. (search for similar items in EconPapers)
Date: 2016
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