May 9, 2016 · The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data. References. [1].
In this paper, an improved- version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent ...
An improved-version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent itemsets over big ...
frequent pattern mining is an essential data mining technique. It aims to discover frequently co-occurring items, correlations and interesting information from ...
The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data. References 29. Citations 2.
Compressed Bitmaps Based Frequent Itemsets Mining on Hadoop. A. Saeed, A. Rauf, S. Khusro, and S. Mahfooz. INFOS, page 159-165. ACM, (2016 ). 1. 1 ...
Compressed Bitmaps Based Frequent Itemsets Mining on Hadoop. https://doi.org ... An Efficient Implementation of Apriori Algorithm Based on Hadoop-Mapreduce Model, ...
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A substantial frequent itemset mining algorithms and their mapreduce implementations are introduced and investi-gated and an algorithm improvement is ...
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This paper reviews Hadoop- and Spark-based scalable algorithms addressing the frequent itemset mining problem in the Big Data domain through both theoretical ...
This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain.