Chameleon |
15 |
15 - 1000 |
30, 60, 90, 150 |
single |
Assess
performance under different combinations of neighbourhood size and
number of sub-partitions |
3 |
chaining increased with increasing number
of sub-partitions over 30, mis-classification rate increased with
neighbourhood size, no clear patterns in within-cluster homogeneity with
either neighbourhood size or number of sub-partitions |
Chameleon |
15 |
15 - 1000 |
agglomerative phase omitted |
NA |
Assess
performance under different neighbourhood size with no agglomerative
phase |
4 |
mis-classification rate decreased strongly,within-cluster
homogeneity decreased weakly with increasing neighbourhood
size |
Chameleon |
15 |
30 |
30, 60, 120, 180, 240, 300, 400, 500 |
complete |
Assess effect of increasing number of sub-partitions on a 15-cluster
solution with neighbourhood size of 30 samples |
4 |
mis-classification
rate decreased weakly, within-cluster homogeneity decreased strongly
with increasing neighbourhood size |
Chameleon |
15, 30, 60, 90, 120, 150 200, 250 |
30 |
agglomerative phase
omitted |
NA |
Assess performance on cluster solutions of different
thematic scale, neighbour size fixed, agglomerative phase omitted |
5,6,7 |
mis-classification rate and within-cluster homogeneity increased
with increasing thematic detail, cluster solutions relatively even in
size |
Chameleon |
15, 30, 60, 90, 120, 150 200, 250 |
30 |
15, 30, 60, 120,
180, 240, 300, 400, 500 |
complete |
Assess performance on cluster
solutions of different thematic scale, neighbour size fixed, number of
sub-partitions proportional to number of final clusters |
5,6,7 |
mis-classification rate and within-cluster homogeneity increased with
increasing thematic detail, cluster solutions relatively even in
size |
Chameleon |
15, 30, 60, 90, 120, 150 200, 250 |
1000 |
30, 60, 120, 180,
240, 300, 400, 500 |
complete |
Assess performance on cluster solutions
of different thematic scale, neighbour size fixed, number of
sub-partitions proportional to number of final clusters |
5,6,7 |
mis-classification rate and within-cluster homogeneity increased with
increasing thematic detail, cluster solutions relatively even in
size |
k-means |
15, 30, 60, 90, 120, 150 200, 250 |
NA |
NA |
NA |
Assess
performance of k-means algorithm over cluster solutions of different
thematic scale |
5,6,7 |
mis-classification rate and within-cluster
homogeneity increased with increasing thematic detail, cluster solutions
relatively even in size |
flexible unweighted pair-group averaging with arithmetic mean (Belbin
et al. 1992) |
15, 30, 60, 90, 120, 150 200, 250 |
NA |
NA |
complete |
Assess performance of agglomerative algorithm over cluster
solutions of different thematic scale |
5,6,7 |
mis-classification rate
and within-cluster homogeneity increased with increasing thematic
detail, cluster solutions relatively uneven in size |
polythetic-division (MacNaughton-Smith et al., 1965; Belbin
et al., 1984) |
15, 30, 60, 90, 120, 150 200, 250 |
NA |
NA |
complete |
Assess performance of divisive algorithm over cluster
solutions of different thematic scale |
5,6,7 |
mis-classification rate
and within-cluster homogeneity increased with increasing thematic
detail, cluster solutions relatively uneven in size |