A Comparison of K-Means Clustering Algorithm and CLARA Clustering Algorithm on Iris Dataset

  • Authors

    • Tanvi Gupta Manav Rachna International Institute of Research and Studies
    • Supriya P. Panda Manav Rachna International Institute of Research and Studies
    2019-02-15
    https://doi.org/10.14419/ijet.v7i4.21472
  • K-Means Clustering, CLARA Clustering, K-Medoids Clustering, PAM Algorithm, Iris Dataset.

  • Abstract

    K-Means Clustering is the clustering technique, which is used to make a number of clusters of the observations. Here the cluster’s center point is the ‘mean’ of that cluster and the others points are the observations that are nearest to the mean value. However, in Clustering Large Applications (CLARA) clustering, medoids are used as their center points for cluster, and rest of the observations in a cluster are near to that center point .Actually in this, clustering will work on large datasets as compared to K-Medoids and K-Means clustering algorithm, as it will select the random observations from the dataset and perform Partitioning Around Medoids (PAM) algorithm on it. This paper will state that out of the two algorithms; K-Means and CLARA, CLARA Clustering gives better result.

     

  • References

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  • How to Cite

    Gupta, T., & P. Panda, S. (2019). A Comparison of K-Means Clustering Algorithm and CLARA Clustering Algorithm on Iris Dataset. International Journal of Engineering & Technology, 7(4), 4766-4768. https://doi.org/10.14419/ijet.v7i4.21472

    Received date: 2018-10-10

    Accepted date: 2019-02-01

    Published date: 2019-02-15