#Desired Clusters: 3 #Clustering Algorithm: Means(1) #Distance Metric: RMS #Matrix Calculation Time: 0 seconds #Total Calculation Time: 61 seconds #Cluster 0 has average-distance-to-centroid 2.270509 #Cluster 1 has average-distance-to-centroid 2.252264 #Cluster 2 has average-distance-to-centroid 2.243106 #DBI: 1.22856 #pSF: 156.67043 #SSR/SST: 0.44111 #Clustering: 3 clusters .............................................................XX.XXXXXXXXXXXXXXXXXXXXXXXX..XXXXXXXXXXXXXXXXXXX.XX...XX.X...XXXXXXXX.............X...X....X..X..XX.XXXX....................................XX.XXXX.........X.....X.XXX.X..XXXX..XXXXXX.XXXX.XXXXXXXXXXXXXXXXXXXXXXXXXXXXX.....X.XXX.XXXX..XXXX......................................X................X............................................ .............................................................................................................X..XXX..X.XXX........XXXXXXXXXXXXX.XXX.XXXX.XX.XX..X....XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX..X....XXXXXXXXX.XXXXX.X...X.XX....XX......X....X.............................XXXXX.X...X....XX....XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.XXXXXXXXXXXXXXXX.XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX..X........................XX...................................................................................................................................................................................................................................................................................................................... ################################################################################## #Clustering: divide 400 points into 3 clusters #Cluster 0: has 137 points, occurence 0.343 #Cluster 1: has 199 points, occurence 0.497 #Cluster 2: has 64 points, occurence 0.160 #Cluster 0 (0) 1 .................................................. #Cluster 1 (1) 1 .................................................. #Cluster 2 (2) 1 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Consensus 1 22222222222222222222222222222222222222222222222222 #Cluster 0 (0) 51 ...........XX.XXXXXXXXXXXXXXXXXXXXXXXX..XXXXXXXXXX #Cluster 1 (1) 51 .................................................. #Cluster 2 (2) 51 XXXXXXXXXXX..X........................XX.......... #Consensus 51 22222222222002000000000000000000000000220000000000 #Cluster 0 (0) 101 XXXXXXXXX.XX...XX.X...XXXXXXXX.............X...X.. #Cluster 1 (1) 101 .........X..XXX..X.XXX........XXXXXXXXXXXXX.XXX.XX #Cluster 2 (2) 101 .................................................. #Consensus 101 00000000010011100101110000000011111111111110111011 #Cluster 0 (0) 151 ..X..X..XX.XXXX................................... #Cluster 1 (1) 151 XX.XX.XX..X....XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Cluster 2 (2) 151 .................................................. #Consensus 151 11011011001000011111111111111111111111111111111111 #Cluster 0 (0) 201 .XX.XXXX.........X.....X.XXX.X..XXXX..XXXXXX.XXXX. #Cluster 1 (1) 201 X..X....XXXXXXXXX.XXXXX.X...X.XX....XX......X....X #Cluster 2 (2) 201 .................................................. #Consensus 201 10010000111111111011111010001011000011000000100001 #Cluster 0 (0) 251 XXXXXXXXXXXXXXXXXXXXXXXXXXXXX.....X.XXX.XXXX..XXXX #Cluster 1 (1) 251 .............................XXXXX.X...X....XX.... #Cluster 2 (2) 251 .................................................. #Consensus 251 00000000000000000000000000000111110100010000110000 #Cluster 0 (0) 301 ......................................X........... #Cluster 1 (1) 301 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.XXXXXXXXXXX #Cluster 2 (2) 301 .................................................. #Consensus 301 11111111111111111111111111111111111111011111111111 #Cluster 0 (0) 351 .....X............................................ #Cluster 1 (1) 351 XXXXX.XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Cluster 2 (2) 351 .................................................. #Consensus 351 11111011111111111111111111111111111111111111111111 ############################################################################################ # Condensed Map # # 400 points divided into 50 windows, each window contains 8 points. # occurence == 0.0 # 0.0 <= occurence < 0.1 . # 0.1 <= occurence < 0.2 1 # 0.2 <= occurence < 0.3 2 # 0.3 <= occurence < 0.4 3 # 0.4 <= occurence < 0.5 4 # 0.5 <= occurence < 0.6 5 # 0.6 <= occurence < 0.7 6 # 0.7 <= occurence < 0.8 7 # 0.8 <= occurence < 0.9 8 # 0.9 <= occurence < 1.0 9 # 1.0 == occurence X # #Clustering: divide 400 points into 3 clusters #Cluster 0: has 137 points, occurence 0.343 #Cluster 1: has 199 points, occurence 0.497 #Cluster 2: has 64 points, occurence 0.160 #Cluster 0 2XXX7X83721155 7 25788XX8365 1 1 #Cluster 1 16278855XXXX2X75211 1635XXXX8X8XXXXX #Cluster 2 XXXXXXX7 2