Outlier Detection using Clustering Techniques

  • Authors

    • Srividya .
    • S Mohanavalli
    • N Sripriya
    • S Poornima
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16508
  • Outliner Detection, Data Mining, K Means, LOF, CLARA

  • Abstract

    An outlier is nothing but a pattern that is different compared to the other existing  patterns in a particular dataset. In some applications it is very important to understand and identify outliers. Detecting outlier is of major importance in many of the fields like cybersecurity, machine learning, finance, healthcare, etc., A clustering based method is proposed to detect outliers using different algorithms like k means, PAM, Clara, DBScan and LOF on different data sets like breast cancer, heart diseases, multi shaped datasets. This work aims to identify the best suitable method to detect the outliners accurately.

     

     

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  • How to Cite

    ., S., Mohanavalli, S., Sripriya, N., & Poornima, S. (2018). Outlier Detection using Clustering Techniques. International Journal of Engineering & Technology, 7(3.12), 813-818. https://doi.org/10.14419/ijet.v7i3.12.16508

    Received date: 2018-07-29

    Accepted date: 2018-07-29

    Published date: 2018-07-20