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Cited by 87Data Mining Algorithms and ApplicationsAdvanced Database Systems and QueriesPrivacy-Preserving Technologies in Data

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A distributed frequent itemset mining algorithm using Spark for Big Data analytics

Zhang, Feng, Liu, Min, Gui, Feng, Shen, Weiming, Shami, Abdallah, Ma, Yunlong·2015·Cluster Computing
DOI 10.1007/s10586-015-0477-1
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Crossref · 10.1007/s10586-015-0477-1

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  • To perform proper sentiment analysis, algorithms need to be able to understand the context and decipher the exact meaning of a word in that context.

    “In order to perform a proper sentiment analyzes, advocates assert that algorithms need to be able to understand the context and be able to decipher the exact meaning of a word in that context (Zhang, Hu et al., 2015).”

    From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Zhang, Hu et al., 2015

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Research works printed but not linked: 42

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Metadata

Authors
Zhang, F., Liu, M., Gui, F., Shen, W., Shami, A., & Ma, Y.
Affiliations
  • Tongji University (CN)
  • Western University (CA)
Corresponding author
Ma, Yunlong
ORCID
Venue
Cluster Computing
Publisher
Springer Science and Business Media LLC
Published
2015-10-28
Volume
18
Issue
4
Pages
1493-1501
ISSN
  • 1386-7857 (ISSN-L)
  • 1573-7543
DOI
10.1007/s10586-015-0477-1
OpenAlex
W2180880586
Cited by
87
References
0
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 12 · 2021: 13 · 2020: 5 · 2019: 11 · 2018: 12 · 2017: 15 · 2016: 5
FWCI
28.79
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Cluster Computing (publishedVersion)
Funders
National Natural Science Foundation of China (61173015)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
frequent itemset mining, distributed mining, Apache Spark, association rule mining, FP-Growth algorithm, big data analytics, scalability

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