BOS | NY | DC | MIA | CHI | SEA | SF | LA | DEN | |
BOS | 0 | 206 | 429 | 1504 | 963 | 2976 | 3095 | 2979 | 1949 |
NY | 206 | 0 | 233 | 1308 | 802 | 2815 | 2934 | 2786 | 1771 |
DC | 429 | 233 | 0 | 1075 | 671 | 2684 | 2799 | 2631 | 1616 |
MIA | 1504 | 1308 | 1075 | 0 | 1329 | 3273 | 3053 | 2687 | 2037 |
CHI | 963 | 802 | 671 | 1329 | 0 | 2013 | 2142 | 2054 | 996 |
SEA | 2976 | 2815 | 2684 | 3273 | 2013 | 0 | 808 | 1131 | 1307 |
SF | 3095 | 2934 | 2799 | 3053 | 2142 | 808 | 0 | 379 | 1235 |
LA | 2979 | 2786 | 2631 | 2687 | 2054 | 1131 | 379 | 0 | 1059 |
DEN | 1949 | 1771 | 1616 | 2037 | 996 | 1307 | 1235 | 1059 | 0 |
Step 3 can be done in different ways, which is what distinguishes single-link from complete-link and average-link clustering.
BOS | NY | DC | MIA | CHI | SEA | SF | LA | DEN | |
BOS | 0 | 206 | 429 | 1504 | 963 | 2976 | 3095 | 2979 | 1949 |
NY | 206 | 0 | 233 | 1308 | 802 | 2815 | 2934 | 2786 | 1771 |
DC | 429 | 233 | 0 | 1075 | 671 | 2684 | 2799 | 2631 | 1616 |
MIA | 1504 | 1308 | 1075 | 0 | 1329 | 3273 | 3053 | 2687 | 2037 |
CHI | 963 | 802 | 671 | 1329 | 0 | 2013 | 2142 | 2054 | 996 |
SEA | 2976 | 2815 | 2684 | 3273 | 2013 | 0 | 808 | 1131 | 1307 |
SF | 3095 | 2934 | 2799 | 3053 | 2142 | 808 | 0 | 379 | 1235 |
LA | 2979 | 2786 | 2631 | 2687 | 2054 | 1131 | 379 | 0 | 1059 |
DEN | 1949 | 1771 | 1616 | 2037 | 996 | 1307 | 1235 | 1059 | 0 |
BOS/NY | DC | MIA | CHI | SEA | SF | LA | DEN | |
BOS/NY | 0 | 223 | 1308 | 802 | 2815 | 2934 | 2786 | 1771 |
DC | 223 | 0 | 1075 | 671 | 2684 | 2799 | 2631 | 1616 |
MIA | 1308 | 1075 | 0 | 1329 | 3273 | 3053 | 2687 | 2037 |
CHI | 802 | 671 | 1329 | 0 | 2013 | 2142 | 2054 | 996 |
SEA | 2815 | 2684 | 3273 | 2013 | 0 | 808 | 1131 | 1307 |
SF | 2934 | 2799 | 3053 | 2142 | 808 | 0 | 379 | 1235 |
LA | 2786 | 2631 | 2687 | 2054 | 1131 | 379 | 0 | 1059 |
DEN | 1771 | 1616 | 2037 | 996 | 1307 | 1235 | 1059 | 0 |
BOS/NY/DC | MIA | CHI | SEA | SF | LA | DEN | |
BOS/NY/DC | 0 | 1075 | 671 | 2684 | 2799 | 2631 | 1616 |
MIA | 1075 | 0 | 1329 | 3273 | 3053 | 2687 | 2037 |
CHI | 671 | 1329 | 0 | 2013 | 2142 | 2054 | 996 |
SEA | 2684 | 3273 | 2013 | 0 | 808 | 1131 | 1307 |
SF | 2799 | 3053 | 2142 | 808 | 0 | 379 | 1235 |
LA | 2631 | 2687 | 2054 | 1131 | 379 | 0 | 1059 |
DEN | 1616 | 2037 | 996 | 1307 | 1235 | 1059 | 0 |
BOS/NY/DC | MIA | CHI | SEA | SF/LA | DEN | |
BOS/NY/DC | 0 | 1075 | 671 | 2684 | 2631 | 1616 |
MIA | 1075 | 0 | 1329 | 3273 | 2687 | 2037 |
CHI | 671 | 1329 | 0 | 2013 | 2054 | 996 |
SEA | 2684 | 3273 | 2013 | 0 | 808 | 1307 |
SF/LA | 2631 | 2687 | 2054 | 808 | 0 | 1059 |
DEN | 1616 | 2037 | 996 | 1307 | 1059 | 0 |
BOS/NY/ DC/CHI | MIA | SEA | SF/LA | DEN | |
BOS/NY/ DC/CHI | 0 | 1075 | 2013 | 2054 | 996 |
MIA | 1075 | 0 | 3273 | 2687 | 2037 |
SEA | 2013 | 3273 | 0 | 808 | 1307 |
SF/LA | 2054 | 2687 | 808 | 0 | 1059 |
DEN | 996 | 2037 | 1307 | 1059 | 0 |
BOS/NY/ DC/CHI | MIA | SF/LA /SEA | DEN | |
BOS/NY/ DC/CHI | 0 | 1075 | 2013 | 996 |
MIA | 1075 | 0 | 2687 | 2037 |
SF/LA/ SEA | 2054 | 2687 | 0 | 1059 |
DEN | 996 | 2037 | 1059 | 0 |
BOS/NY/DC/ CHI/DEN | MIA | SF/LA/SEA | |
BOS/NY/DC/ CHI/DEN | 0 | 1075 | 1059 |
MIA | 1075 | 0 | 2687 |
SF/LA/SEA | 1059 | 2687 | 0 |
BOS/NY/DC/CHI/ DEN/SF/LA/SEA | MIA | |
BOS/NY/DC/CHI/ DEN/SF/LA/SEA | 0 | 1075 |
MIA | 1075 | 0 |
JOHNSON'S HIERARCHICAL CLUSTERING -------------------------------------------------------------------------------- Method: SINGLE_LINK (minimum distance) Type of Data: Dissimilarities Input dataset: cities (D:\Users\Hyo\Documents\UCINET data\Cities\cities) HIERARCHICAL CLUSTERING M S B C D I E S L O N D H E A A F A S Y C I N Level 4 6 7 8 1 2 3 5 9 ----- - - - - - - - - - 206 . . . . XXX . . . 233 . . . . XXXXX . . 379 . . XXX XXXXX . . 671 . . XXX XXXXXXX . 808 . XXXXX XXXXXXX . 996 . XXXXX XXXXXXXXX 1059 . XXXXXXXXXXXXXXX 1075 XXXXXXXXXXXXXXXXX Measures of cluster adequacy 1 2 3 4 5 6 7 ------ ------ ------ ------ ------ ------ ------ 1 Eta -0.284 -0.480 -0.554 -0.657 -0.711 -0.687 -0.151 2 Q -0.133 -0.163 -0.188 -0.203 -0.240 -0.214 -0.033 3 Q-prime -0.152 -0.190 -0.226 -0.254 -0.320 -0.322 -0.065 4 E-I 0.994 0.973 0.961 0.884 0.824 0.625 -0.490 Size of each cluster, expressed as a proportion of the total population clustered 1 2 3 4 5 6 7 8 ----- ----- ----- ----- ----- ----- ----- ----- 1 CL1 0.222 0.333 0.333 0.111 0.111 0.111 0.111 1.000 2 CL2 0.111 0.111 0.111 0.444 0.444 0.333 0.889 3 CL3 0.111 0.111 0.111 0.111 0.333 0.556 4 CL4 0.111 0.111 0.111 0.222 0.111 5 CL5 0.111 0.111 0.222 0.111 6 CL6 0.111 0.111 0.111 7 CL7 0.111 0.111 8 CL8 0.111 Actor-by-Partition indicator matrix saved as dataset Part ---------------------------------------- Running time: 00:00:01 Output generated: 21 11 16 09:10:06 UCINET 6.614 Copyright (c) 1992-2016 Analytic Technologies
0 206 429 1504 963 2976 3095 2979 1949 206 0 233 1308 802 2815 2934 2786 1771 429 233 0 1075 671 2684 2799 2631 1616 1504 1308 1075 0 1329 3273 3053 2687 2037 963 802 671 1329 0 2013 2142 2054 996 2976 2815 2684 3273 2013 0 808 1131 1307 3095 2934 2799 3053 2142 808 0 379 1235 2979 2786 2631 2687 2054 1131 379 0 1059 1949 1771 1616 2037 996 1307 1235 1059 0
# Prepare Data
setwd(“d:/rdata”)
mydata ← read.csv(“cities.csv”)
mydata ← na.omit(mydata) # listwise deletion of missing
mydata ← scale(mydata) # standardize variables