Abstract
FIM (Frequent item set mining) is a kind of important data analysis and data mining application. It is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. In this paper, we study the parallel FP-Like algorithm and compare with the parallel Apriori algorithm. The experimental result shows that the parallel FP-Like algorithm is high-performance in scalability and speed. It can effectively deal with FIM on big data and low support.
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