An efficient and robust cluster based outlying points detection in multivariate data sets

  • Authors

    • S. Anitha
    • Dr. Mary Metilda
    https://doi.org/10.14419/ijet.v7i4.21505

  • Abstract

    Outlier is a data that does not match to the normal points along with the data set. A recent research focused on number of clusters and distance based outlier detection strategies. In this paper, outliers are identified and eliminated in four phases.Feature selection technique using genetic algorithm is applied to the pre processed data to reducedlarge amount of dataset into significant attributes. Data sets are partitioned as clusters after the feature selection process. Multiple outliers are identified by mahalanobis distance based onthe value of median and covariance matrix. Four real life data sets are taken from UCI machine learning repository and rigorous experiments are conducted by the proposed process of GBFS, CLOPD, and IMO for selecting the relevant subsets, clustering and Outliers removal. These three methods are analysed with data sets and results are depicted. It usedforreducing time complexity and improving clustering and classification accuracy.

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  • How to Cite

    Anitha, S., & Metilda, D. M. (2018). An efficient and robust cluster based outlying points detection in multivariate data sets. International Journal of Engineering & Technology, 7(4), 2881-2885. https://doi.org/10.14419/ijet.v7i4.21505