Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data...

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Veröffentlicht in:	Information retrieval (Boston) Jg. 27; H. 1; S. 38
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Nature B.V 02.11.2024
Schlagworte:	Algorithms Boolean Clustering Data compression Data mining Datasets Distributed processing Efficiency Parallel processing Python Social networks User requirements
ISSN:	1386-4564, 1573-7659
Online-Zugang:	Volltext
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