Character Classes

[MarcoP]

I have made a “character classification” experiment based on the position of characters with respect to each other. In this case, I did not consider the position of characters inside words (e.g. if one character tends to occur as a prefix or a suffix).

I introduced upper case characters corresponding to these Voynichese sequences:
K ckh ckh
T cth cth
P cph cph
F cfh cfh
C ch ch
S sh sh
M iin iin
N in in

For each character, I have computed a set of 10 frequencies (in the range 0..1, corresponding to a percentage on the total number of occurrences of the character):

1. occurrences before a
   
2. occurrences after a
   
3. occurrences before d
   
4. occurrences after d
   
5. occurrences before k
   
6. occurrences after k
   
7. occurrences before l
   
8. occurrences after l
   
9. occurrences before o
   
10. occurrences after o
    

So, for each character, a 10-dimensional vector was generated. The vectors were fed to the K-Means clustering algorithm, that groups vectors on the basis of their distance from each other.
I used the Elki java software with this command:
 java -jar elki-bundle-0.7.1.jar KDDCLIApplication -dbc.in input.txt -algorithm clustering.kmeans.KMedoidsEM -kmeans.k K

I tried several values for K (the number of output classes). The clustering software is of course very affected by the value of this parameter.

These are the results for 4 and 10 clusters:

4 clusters:
# Cluster: Cluster 0
i N M n m 
# Cluster: Cluster 1
t s r p l k f d T P K F
# Cluster: Cluster 2
y o h g e c a S C
# Cluster: Cluster 3
q


10 clusters:
# Cluster: Cluster 0
r l
# Cluster: Cluster 1
g c
# Cluster: Cluster 2
C S h e
# Cluster: Cluster 3
a y
# Cluster: Cluster 4
m n
# Cluster: Cluster 5
T s P p K F
# Cluster: Cluster 6
k t f d
# Cluster: Cluster 7
q
# Cluster: Cluster 8
o
# Cluster: Cluster 9
N i M

I attach a 2D plot based on two of the 10 numeric values I used (X axis “AFTER O %”, Y axis “AFTER L %”). 
For instance, k occurs after o 39% of the times and after l 14% of the times.
Since the plot only represents 1/5 of the numeric values used, the distribution of the clusters might not be completely compatible with what we see in the plot, but it should give an idea of how clustering works: points that are close in the 10-dimensional space are assigned to the same cluster.



For comparison, these are the results I get by applying the same method to the English in King James Bible:

4 clusters:
# Cluster: Cluster 0
y
# Cluster: Cluster 1
z x v t r n m k h
# Cluster: Cluster 2
u s q o i g e d a
# Cluster: Cluster 3
p w l j f c b

10 clusters:
# Cluster: Cluster 0
z
# Cluster: Cluster 1
p l f c b
# Cluster: Cluster 2
e
# Cluster: Cluster 3
u
# Cluster: Cluster 4
w
# Cluster: Cluster 5
k r n m v
# Cluster: Cluster 6
t s q h g d x
# Cluster: Cluster 7
j
# Cluster: Cluster 8
i o a
# Cluster: Cluster 9
y