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I'm a SQLServer DBA and in the new version of this tool, there are new features to integrate R scripts and use it easily with the DB objects. That sounds cool.

But to use that, we have to know a little bit about stats and data mining. And it's quite hard to find friendly and readable documentation for newbies like me. So, I will ask for some best-practice about clustering.

I have a dataset with few hundreds records and with 5 features, both categorical and numerical.

Regarding categorical data, I have a few basic questions :

  • how can I manage categorical data ? what I understood from articles found is that I can transform categorical data in boolean and "pivoting" it. If a have a column with categorical data (for instance with values 'a' or 'b'), creating to new columns like column_a and column_b with value 0 or 1 ?

  • should I always center / scale this new column ?

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  • $\begingroup$ Welcome to the site. I think the question can be broken down into multiple questions. Points 1&2 together, 3, 4, 5 together and the last one separately. :) $\endgroup$
    – Dawny33
    Apr 27, 2016 at 12:50
  • $\begingroup$ Indeed, let's just keep the categorical topic. (title and question have been rephrased) $\endgroup$
    – irimias
    Apr 28, 2016 at 11:37

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You need to create a dissimilarity matrix first and then apply the clustering technique. Below link has many answer referring your point. Hope below will suffice for this:

https://stats.stackexchange.com/questions/24540/clustering-of-mixed-type-data-with-r

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