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DTSTART;TZID=America/Denver:20191117T163000
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UID:submissions.supercomputing.org_SC19_sess112_pec252@linklings.com
SUMMARY:On a Parallel Spark Workflow for Frequent Itemset Mining Based on 
 Array Prefix-Tree
DESCRIPTION:Workshop\n\nOn a Parallel Spark Workflow for Frequent Itemset 
 Mining Based on Array Prefix-Tree\n\nNiu, Qian, Wu, Hou\n\nFrequent Itemse
 t Mining (FIM) is a fundamental procedure in various data mining technique
 s such as association rule mining. Among many existing algorithms, FP-Grow
 th is considered as a milestone achievement that discovers frequent itemse
 ts without generating candidates. However, due to the high complexity of i
 ts mining process and the high cost of its memory usage, FP-Growth still s
 uffers from a performance bottleneck when dealing with large datasets. In 
 this paper, we design a new Array Prefix-Tree structure, and based on that
 , propose an Array Prefix-Tree Growth (APT-Growth) algorithm, which explic
 itly obviates the need of recursively constructing conditional FP-Tree as 
 required by FP-Growth. To support big data analytics, we further design an
 d implement a parallel version of APTGrowth, referred to as PAPT-Growth, a
 s a Spark workflow. We conduct FIM workflow experiments on both real-life 
 and synthetic datasets for performance evaluation, and extensive results s
 how that PAPT-Growth outperforms other representative parallel FIM algorit
 hms in terms of execution time, which sheds light on its potential applica
 tions to big data mining.\n\nTag: Workshop Reg Pass, Extreme Scale Computi
 ng, Scalable Computing, Scientific Workflows\n\nRegistration Category: Wor
 kshop Reg Pass, Extreme Scale Computing, Scalable Computing, Scientific Wo
 rkflows
URL:https://sc19.supercomputing.org/presentation/?id=pec252&sess=sess112
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