Top-k-FCI: Mining Top-K Frequent Closed Itemsets in Data Streams
نویسندگان
چکیده
With the generation and analysis of stream data, such as network monitoring in real time, log records, click streams, a great deal of attention has been concerned on data streams mining in the field of data mining. In the process of the data streams mining, it is more reasonable to ask users to set a bound on the result size. Therefore, in this paper, an real-time single-pass algorithm, called Top-k-FCI (top-K frequent closed itemsets of data streams), is proposed for mining top-K closed itemsets from data streams efficiently. A novel algorithm, called can (T)(candidate itemset of the T), is developed for mining the essential candidate of closed itemsets generated so far. Experimental results show that the proposed Top-k-FCI algorithm is an efficient method for mining top-K frequent itemsets from data streams.
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