A Streamlined Frequent Item set excavating using FP growth from Map Reduce
Keywords:
Frequent item set mining, Hadoop Map Reduce, Parallel FP-Growth, Small files problemAbstract
As a significant part of discovering association rules, frequent item sets excavating plays a key role in removal associations, correlations, bass and other imperative data mining tasks. Since a little customary frequent item sets mining algorithms are incapable to knob gargantuan small file datasets effectively, such as high recall cost, high I/O operating price, and squat computing recitals, a better Parallel FP-Growth (EPFP) algorithm and converse its applications in this paper. In particular, a small file processing strategy for huge small file datasets to reimburse defects of squat read/write speed and low processing efficiency in Hadoop. Moreover, utilize of Map Reduce to execute the parallelization of FP-Growth algorithm, thereby improving the general performance of frequent item set mining. The investigation results demonstrate that the EPFP algorithm is practicable and suitable with a excellent speedup and a higher mining efficiency, and can convene the rapidly growing needs of frequent item sets mining for enormous petite file data sets.
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