About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
ew 21(35): e3

Research Article

Frequent Pattern Retrieval on Data Streams by using Sliding Window

Download974 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eai.13-1-2021.168091,
        author={P. Mahesh Kumar and P. Srinivasa Rao},
        title={Frequent Pattern Retrieval on Data Streams by using Sliding Window},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={8},
        number={35},
        publisher={EAI},
        journal_a={EW},
        year={2021},
        month={1},
        keywords={Frequent Pattern Retrieval Algorithm, Information Extraction, Sliding Window Stream Data, Candidate Patterns},
        doi={10.4108/eai.13-1-2021.168091}
    }
    
  • P. Mahesh Kumar
    P. Srinivasa Rao
    Year: 2021
    Frequent Pattern Retrieval on Data Streams by using Sliding Window
    EW
    EAI
    DOI: 10.4108/eai.13-1-2021.168091
P. Mahesh Kumar1,*, P. Srinivasa Rao2
  • 1: Assistant Professor of CSE, TKR College of Engineering and Technology, Hyderabad, External Research Scholar, CSE, JNTUK, Kakinada, India
  • 2: Associate Professor of CSE, MVGR College of Engineering, Vizianagaram, Andhrapradesh, India
*Contact email: maheshkumarp@tkrcet.com

Abstract

In different applications like recommender frameworks and market examination, regular patterns play a significant role in useful mining data. Mining regular patterns from sliding windows over streaming information has become a complex task. In this examination, the sliding window is utilized to build the framework and FP tree applied to mine the dataset's valuable data. The sliding window has the arrangement of patterns put away in the Matrix, which contains the transaction in the sliding information and thenapplied to the FP tree. In this paper, the Frequent Pattern Retrieval strategy is planned by utilizing anFP tree approach and a sliding window model to extract noteworthy examples from data streams. The proposed technique accomplished less runtime with low memory use for the Breast disease dataset and different datasets to run the least utility edge contrasted with different existing procedures.

Keywords
Frequent Pattern Retrieval Algorithm, Information Extraction, Sliding Window Stream Data, Candidate Patterns
Received
2020-11-05
Accepted
2021-01-09
Published
2021-01-13
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-1-2021.168091

Copyright © 2021 P. Mahesh Kumar et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL