I have a data table where the entries are in the following format. The first column is category, which represent the product category. I have 5 such categories. Feature 1,2 and 3 are different features associated with each of these categories. These features may be either categorical or numerical.

I am planning to apply multivariate outlier detection mvoutlier in R package to explore this data.

I have following questions that I am trying to answer:

  1. Should I consider each of these categories separately and perform the analyses independent of other categories?

  2. How to handle the categorical values in this data?


category    f1  f2  f3
a1  1   33.4    333
a1  0   23  444
a1  0   30  333
a1  0   34  300
a2  1   56  600
a2  1   60  609
a2  1   64  630
a2  1   66  650
a3  0   99  900
a1  0   30  320
a3  0   99  1000
a1  0   30  340
a2  0   59  600

For mixed data (containing both numerical and categorical features), you could perform discretisation of numerical features into categories (see, for example, this paper). Then all your features will be categorical and you can use one-hot encoding (also called conversion to the vector space, please see https://stats.stackexchange.com/a/52917/67464 for details) , to apply outlier detection methods on these data.


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