[EMAIL PROTECTED] wrote:
>
> I am reading a book called Data Preparation for Data Mining by Dorian
> Pyle. I am having problem understanding Section 7.2.3 called
> Adjusting Distributions:
>
> "The easiest way to adjust a distribution density is simply to displace
> the high density points into the low density areas until all points
> are at the mean density for the variable. Such a process ends up
> in a rectangular distribution. This simple approach can only be
> completely
> successful if none of the instance values is duplicted.........In effect
> every point is
> displaced in a particular direction and distance. Any point in
> the variable's range could be used as a reference. The zero point
> is a convenient as any other. Using this as a reference every other
> point can be specified as moving away from or toward the reference
> point."
>
> Can anyone elaborate of Dorian's explanantion??????
The author appears to be making the ridiculous assertion that you should
use a rectangular distribution in most instances, regardless of what
your original distribution looked like, which he says can be successful
unless you are in the unfortunate case where one of the values is
duplicated. I have no idea what the criterion for "successful" is that
he refers to, but it would seem this is a necessary condition to be
"successful", but I highly doubt it is a sufficient condition.
Nevertheless, if you want to adjust your normal distribution to a
rectangular one, or your highly skewed distribution to a rectangular
one, you can be "successful". Good luck!
--
Paige Miller
Eastman Kodak Company
[EMAIL PROTECTED]
"It's nothing until I call it!" -- Bill Klem, NL Umpire
"Those black-eyed peas tasted all right to me" -- Dixie Chicks
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