An Accelerator for Frequent Itemset Mining from Data Streams with Parallel Item Tree
نویسندگان
چکیده
Frequent itemset mining attempts to find frequent subsets in a transaction database. In this era of big data, demand for frequent itemset mining is increasing. Therefore, the combination of fast implementation and low memory consumption, especially for stream data, is needed. In response to this, we optimize an online algorithm, called Skip LC-SS algorithm [1], for hardware. In this paper, we present an efficient architecture based on this algorithm. Keywords—data mining; frequent itemsets; stream processing; hardware accelerator
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تاریخ انتشار 2015