Clustering : handling categorical data, should we pivot and scale? 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 ?
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