• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Peer Review Process
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
International Journal of Intelligent Computing and Information Sciences
arrow Articles in Press
arrow Current Issue
Journal Archive
Volume Volume 20 (2020)
Volume Volume 19 (2019)
Volume Volume 18 (2018)
Volume Volume 17 (2017)
Volume Volume 16 (2016)
Volume Volume 15 (2015)
Volume Volume 14 (2014)
sayed, D., Aref, M., rady, S. (2020). SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING. International Journal of Intelligent Computing and Information Sciences, (), -. doi: 10.21608/ijicis.2020.18625.1011
doaa ahmed sayed; M mahmoud Aref; Sherine rady. "SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING". International Journal of Intelligent Computing and Information Sciences, , , 2020, -. doi: 10.21608/ijicis.2020.18625.1011
sayed, D., Aref, M., rady, S. (2020). 'SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING', International Journal of Intelligent Computing and Information Sciences, (), pp. -. doi: 10.21608/ijicis.2020.18625.1011
sayed, D., Aref, M., rady, S. SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING. International Journal of Intelligent Computing and Information Sciences, 2020; (): -. doi: 10.21608/ijicis.2020.18625.1011

SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING

Articles in Press, Accepted Manuscript, Available Online from 29 June 2020  XML
Document Type: Original Article
DOI: 10.21608/ijicis.2020.18625.1011
Authors
doaa ahmed sayed email 1; M mahmoud Aref2; Sherine rady3
1computer science , faculty of computer and information,Ain shimas
2Department Computer Science, Faculty of Computer and Information Sciences,Ain Shams University, Cairo, Egypt.
3Information Systems,Department, Faculty of Computer and Information Sciences,Ain Shams university,cairo,Egypts
Abstract
Mining in data streams has been a hot research topic in the recent time. A main challenge in data stream mining lies in extracting knowledge in real time from a massive, dynamic data stream in only a single scan. Data stream clustering presents an important role in data stream processing. This paper proposes SCluStream an algorithm for tracking clusters over a sliding window to handle such challenges. The algorithm is an enhancement over CluStream which does not involve this sliding window concept. In the sliding window model, only the most recent data is used while the old data is eliminated, which allows for faster execution. A better clustering technique is also involved which managed to contribute to accuracy enhancement. The proposed algorithm has been tested on a dataset for Intrusion detection and the results showed that comparing SCluStream to CluStream has proven that the former algorithm is more efficient for online clusters generation for big data streaming in regard of the accuracy as well as the utilized time and memory resources.
Keywords
Data stream mining; Data stream clustering; Time series in big data; Window models; sliding window
Statistics
Article View: 54
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Journal Management System. Designed by NotionWave.