Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms

This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on Hadoop clusters (MR-SSFIM). EA-SSFIM and MR-SSFI...

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Bibliographic Details
Published in:IEEE access 2018, Vol.6, p.68013-68026
Main Authors: Djenouri, Youcef, Djenouri, Djamel, Lin, Jerry Chun-Wei, Belhadi, Asma
Format: Article
Language:English
Subjects:
Algorithms
Apriori
Big Data
Clustering algorithms
Computer science
Data mining
frequent itemset mining
heuristic
Itemsets
parallel computing
Runtime
support computing
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Summary:This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on Hadoop clusters (MR-SSFIM). EA-SSFIM and MR-SSFIM target sparse and big databases, respectively. The proposed approach (in all its variants) requires only one scan to extract the candidate itemsets, and it has the advantage to generate a fixed number of candidate itemsets independently from the value of the minimum support. This accelerates the scan process compared with existing approaches while dealing with sparse and big databases. Numerical results show that SSFIM outperforms the state-of-the-art FIM approaches while dealing with medium and large databases. Moreover, EA-SSFIM provides similar performance as SSFIM while considerably reducing the runtime for large databases. The results also reveal the superiority of MR-SSFIM compared with the existing HPC-based solutions for FIM using sparse and big databases.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2880275