Transcript Traveller mode in Code Vector Activity Detection based GLA

Clustering Methods 2010
Aki Heikkinen
Centroid are classified into states [2]:
 Active
 Static
Data point classifications [2]:
 Static, when the centroid is static
 Balanced, when the centroid is active, but the distance between
centroid and data point didn’t change
 Farther, when the centroid is active and it moves away from the
datapoint
 Closer, when the centroid is active and it moves closer to the
datapoint
Algorithm improvement:
When data point is in ’closer state’ (ie. current centroid
has moved closer to the data point) instead of searching all
active centroids, seach only the current centroid and all the other
active centroids that have moved greater distance than the
current centroid [1].
Centroids moving greater distance are ”travellers”
D1 < D6? ok
D6
D2
D1 < D2? ok
Closer data point
D1 < D5? NO!
D1
D5
D1 < D3? ok
D3
Search the nearest centroid!
D4
D1 < D4? NO!
Specs:
Average of 500 runs
100 swaps
2 K-Mean iterations
S1-Dataset
MSE
TIME
Default
Traveller search
0,89875
0,90456
0,48281
0,46938
S2-Dataset
MSE
TIME
Default
Traveller search
1,33087
1,32932
0,54935
0,52371
Specs:
Average of 100 runs
100 swaps
2 K-Mean iterations
Birch1-Dataset
MSE
TIME
Default
Traveller search
4,74680
4,74619
31,77298
30,74745
Specs:
Average of 100 runs
100 swaps
20 K-Mean iterations
S1-Dataset
MSE
TIME
Default
Traveller search
0,89176
0,89176
1,41334
1,40127
S2-Dataset
MSE
TIME
Default
Traveller search
1,32791
1,32791
2,25996
2,22452
Specs:
Average of 100 runs
100 swaps
50 K-Mean iterations
S1-Dataset
MSE
TIME
Default
Traveller search
0,89176
0,89176
1,50824
1,49933
S2-Dataset
MSE
TIME
Default
Traveller search
1,32791
1,32791
2,46833
2,43418
Specs:
Average of 500 runs
K-Mean algorithm
S1-Dataset
MSE
TIME
Default
Traveller search
1,87835
1,89514
0,04246
0,04172
S2-Dataset
MSE
TIME
Default
Traveller search
1,98499
1,99212
0,05895
0,05600
[1] Kuo-Liang Chung, Jhin-Sian Lin, Faster and more
robust point symmtery-based K-means algorithm,
Pattern Recognition, 40, 410-422, 2007.
[2] T. Kaukoranta, P. Fränti, O. Nevalainen, A fast exact
GLA based on code vector activity detection, IEEE Trans.
on Image Processing, 9 (8), 1337-1342, August 2000.