Character Classes

[MarcoP]

Thanks, Marco! I wish I was more literate in statistics, so I could understand more - could you tell me what you found in a more plaintext format?

Hi Thomas, I wouldn't say I have found anything, but maybe this approach could provide something helpful, if applied in a less random way. The basic concept is simple. Each character is mapped to a vector of 10 numbers. Each vector can be imagined as a point in a 10-dimensional space (a vector of 2 numbers corresponds to a point in 2D, 3 numbers to a point in space...).
The K-Means algorithm splits the set of 10 dimensional points into K groups (called "clusters") made up of points that are close to each other.

So, characters that are assigned to the same cluster are in some way "similar" to each other.

For instance, considering the King James Bible example:

# Cluster: Cluster 1
p l f c b
groups a subset of the consonants, including 3 labials.

# Cluster: Cluster 8
i o a
groups three vowels

Other clusters from the King James Bible are more difficult to analyze. At best, we can say that the 10 Cluster example seems to correctly separate vowels from consonants, but the grouping of consonants is often unclear.
If one could define a more meaningful way of assigning numeric vectors to Voynichese characters, probably we would get more reliable results. The groupings produced by this experiment in its current form must be taken with care

---

Interesting... can I ask why you chose these five letters?

Hi Sam,
the choice of these letters was rather random:
I picked o and l because ol is the most common sequence of two characters.
d because it often combines with ol as both old and dol
k because it's the most common gallow.
Finally I added a because it's another common character.

As far as the results, I found it surprising that g is more common after l than it is after o.

I should have mentioned that the numbers are based on the "dictionary" of words, not occurrences in the MS.
lg occurs in 4 words, og in 3 words. These numbers are so small that they are likely irrelevant.