[PDF] euclidean vs correlation clustering

10 sept. 2020 · There is neither a guide nor standard for this. If using either of Euclidean distance or Pearson correlation, your data should follow a  Autres questions
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  • What is the difference between correlation and Euclidean distance?

    Whereas euclidean distance was the sum of squared differences, correlation is basically the average product.
    There is a further relationship between the two.

  • What is the difference between correlation and clustering?

    Clustering is the problem of partitioning data points into groups based on their similarity.
    Correlation clustering provides a method for clustering a set of objects into the optimum number of clusters without specifying that number in advance.

  • When would you prefer Euclidean distance over correlation based distance methods?

    If you want to identify clusters of observations with the same overall profiles regardless of their magnitudes, then you should go with correlation-based distance as a dissimilarity measure.
    If Euclidean distance is chosen, then observations with high values of features will be clustered together.

  • When would you prefer Euclidean distance over correlation based distance methods?

    In mathematics, the Euclidean distance or Euclidean metric is the "ordinary" straight-line distance between two points in Euclidean space.
    Pearson: Pearson Correlation measures the similarity in shape between two profiles.

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