I find this taxonomy excessive and over-done. The distinctions I find useful include
- continuous variables - discrete variables with a known set of values (I call these categorical, usually). This includes ordinal variables since ordering rarely makes a lot of difference. - discrete variables with a large or not well known set of possible values (I call these "word-like") - bags or lists of word-like variables (I call these text-like) Occasionally, I also use - bags of (word, time, amount) triples where time and amount are continuous variables. I call these transactions. Most of the rest is fluff. You might want it for algebraic completeness and ability to describe absolutely everything, but it really doesn't much matter for practical purposes. On Tue, Nov 29, 2011 at 12:08 AM, Konstantin Shmakov <[email protected]>wrote: > It is missing definition of "atom" (at least the page referred to); is it > the basic piece of information? > > It is also seems that "numeric" is continuous (temperature, fin data) and > "categoric" and "ordinal" are discrete (words, ratings). > > As such all these data types will be more naturally categorized along 3 > dimensions: > - continuous, discrete > - ordered, unordered > - data dimensionality (1d, 2d, 3d) > > -- > > > On Mon, Nov 28, 2011 at 8:25 PM, Lance Norskog <[email protected]> wrote: > > > Is this a fair breakdown of data classes? > > > > http://smlv.cc.gatech.edu/2010/03/23/a-taxonomy-of-data-types/ > > > > (btw everything tagged DAVA is interesting) > > > > -- > > Lance Norskog > > [email protected] > > > > > > -- > ksh: >
