fig3a.fig

一种数据聚类算法的源码

#FIG 3.2
Landscape
Center
Inches
Letter  
100.00
Single
-2
1200 2
1 1 0 2 0 7 50 0 -1 0.000 1 0.0000 3450 4500 300 150 3450 4500 3750 4500
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 4200 1800 4800 1800
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 5550 2250 5550 2850
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 4800 3300 4200 3300
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 3450 2850 3450 2250
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 3450 3750 3450 4350
2 2 0 2 0 7 50 0 -1 0.000 0 0 -1 0 0 5
	 2700 2850 4200 2850 4200 3750 2700 3750 2700 2850
2 2 0 2 0 7 50 0 -1 0.000 0 0 -1 0 0 5
	 4800 2850 6300 2850 6300 3750 4800 3750 4800 2850
2 2 0 2 0 7 50 0 -1 0.000 0 0 -1 0 0 5
	 4800 1350 6300 1350 6300 2250 4800 2250 4800 1350
2 2 0 2 0 7 50 0 -1 0.000 0 0 -1 0 0 5
	 2700 1350 4200 1350 4200 2250 2700 2250 2700 1350
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 2100 2100 2700 2100
2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 2100 1500 2700 1500
4 0 0 50 0 16 10 0.0000 4 150 1215 2775 1950 pixel using cluster\001
4 0 0 50 0 16 10 0.0000 4 150 765 2775 2145 parameters\001
4 0 0 50 0 16 10 0.0000 4 120 900 2775 1575 Perform soft\001
4 0 0 50 0 16 10 0.0000 4 120 1425 2775 1770 classification of each\001
4 0 0 50 0 16 10 0.0000 4 150 1290 2775 3075 Measure goodness\001
4 0 0 50 0 16 10 0.0000 4 150 1410 2775 3270 of fit using Rissanen\001
4 0 0 50 0 16 10 0.0000 4 150 1380 2775 3660 far,then save result.\001
4 0 0 50 0 16 10 0.0000 4 120 1350 4875 1575 Re-estimate cluster\001
4 0 0 50 0 16 10 0.0000 4 150 1410 4875 2160 covariance matrices)\001
4 0 0 50 0 16 10 0.0000 4 150 810 4875 1770 parameters.\001
4 0 0 50 0 16 10 0.0000 4 120 1245 4875 3075 Reduce number of\001
4 0 0 50 0 16 10 0.0000 4 150 1200 3600 4125 Only one cluster?\001
4 0 0 50 0 16 10 0.0000 4 150 1290 4875 1965 (mean vectors and\001
4 0 0 50 0 16 10 0.0000 4 120 1515 2775 3465 criterion. If best fit so\001
4 0 0 50 0 16 10 0.0000 4 150 735 4875 3270 clusters by\001
4 0 0 50 0 16 10 0.0000 4 150 1020 4875 3465 combining two\001
4 0 0 50 0 16 10 0.0000 4 150 765 975 2175 parameters\001
4 0 0 50 0 16 10 0.0000 4 120 1035 975 2025 Initialize cluster\001
4 0 0 50 0 16 10 0.0000 4 120 720 975 1650 of clusters\001
4 0 0 50 0 16 10 0.0000 4 120 1095 975 1500 Initialize number\001
4 1 0 50 0 16 10 0.0000 4 120 285 3427 4560 Exit\001
4 0 0 50 0 16 10 0.0000 4 120 1125 4875 3653 nearest clusters.\001