MFI-TransSW+: Efficiently Mining Frequent Itemsets in Clickstreams

Data stream mining is the process of extracting knowledge from massive real-time sequence of data items arriving at a very high data rate. It has several practical applications, such as user behavior analysis, software testing and market research. However, the large amount of data generated may offer challenges to process and analyze data at nearly real time. In this paper, we first present the MFI-TransSW+ algorithm, an optimized version of MFI-TransSW algorithm that efficiently processes clickstreams, that is, data streams where the data items are the pages of a Web site. Then, we outline the implementation of a news articles recommender system, called ClickRec, to demonstrate the efficiency and applicability of the proposed algorithm. Finally, we describe experiments, conducted with real world data, which show that MFI-TransSW+ outperforms the original algorithm, being up to two orders of magnitude faster when processing clickstreams.

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