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An Optimized Approach of Outlier Detection Algorithm for Outlier Attributes on Data Streams

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Madhu Shukla

    (Marwadi Education Foundation, Department of Computer Engineering
    RK University)

  • Y. P. Kosta

    (Marwadi Education Foundation)

Abstract

Advancement in technology has made systems integrated, connected and communicating with each other, so amount of data generated in day to day life has been increasing in leaps and bounds. IOT is the best example of such kind of systems and it has opened new gates of research on such data generation and analysis. Data generated by non-stationary system are fast, huge, and continuous in nature. These data are termed as data streams. Mining of these kind of data has inbuilt challenges as they possess different characteristics. Traditional algorithms are not well suited for these kind of data. Also, Mining data streams to classify outlier attribute becomes a more tedious task as data arrives continuously. Also, multiple scans of stream data is not possible due to its huge size. Hence, to address above said issues, changes in the structure of algorithm needs to be done. In this paper, a modified approach on outlier detection method MCOD has been discussed and proposed which gives improved results in terms of outlier attribute detection.

Suggested Citation

  • Madhu Shukla & Y. P. Kosta, 2020. "An Optimized Approach of Outlier Detection Algorithm for Outlier Attributes on Data Streams," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 711-724, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_70
    DOI: 10.1007/978-3-030-41862-5_70
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