Color iQC Help - X-Rite

applying the taper process. This method of clustering non-uniform data sets
before tapering, either by linear or minimum path method, is generally thought to
be more effective than simply allowing the computer to treat breaks as new taper
sequences.
Linear Grouping
SLI-Taper/W has two other ways to group the data set so that more
than one taper can be performed. The first is linear grouping. Two options exist
for this method. The data set can be divided into two or three linear groups. This
method of dividing the data set relies on the same calculation as a linear taper,
that is, determining the line-of-best-fit. Dividing the data into two groups is quite
simple. The line-of-best-fit is calculated through the data set and then all the
points that fall above that line are one group and all the points that fall below that
line are the second. These two groups may then be tapered by either the linear
or minimum path method. (Linear grouping is just that, grouping. It does not imply
that the linear method of tapering must be used.) Dividing the data set into three
groups is a bit more complicated. Again, the line-of-best-fit must be calculated.
However, for three groups, two more lines are placed into the data set, one
above and one below, parallel to the line-of-best-fit. The three groups are then
formed by the samples above the top line, the samples below the bottom line,
and the samples between the two parallel lines. As with two linear groups, these
three groups may be tapered by either the linear or the minimum path method.
LMD Grouping
The second method of dividing up a data set for tapering is through LMD
grouping. As with the linear grouping, SLI-Taper/W can group data into two or
three LMD groups depending on what the user chooses. This method uses the
lightness value of the data to divide it up into light, medium, and dark samples
(hence the term LMD grouping). Limits in dEcmc units which define how these
groups are formed are either specified by the user or the program may be set to
calculate these from the data. That is, how many dEcmc units must the program
move upwards from the lightest sample before it should consider the samples it
is encountering to be of medium lightness, and then dark. Obviously, when
dividing the data into only two groups, the medium lightness value is omitted.
BSort Grouping
The third method is called B sort grouping. This method of dividing up
the data set uses the b* value for each sample, comparing it to the standard. This
method is especially useful for denim. It divides the data set up according to
whether or not it tends towards the yellow end of the b* axis or the blue end of
the b* axis. You may select to group the data into two or three BSort groups, or
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