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--- johnson_s_hierarchical_clustering [2016/11/21 09:09] – hkimscil
+++ johnson_s_hierarchical_clustering [2016/11/21 12:45] (current) – [E.g. 1] hkimscil
@@ Line 99: / Line 99: @@
 {{:hiclus1.gif}}
+<code>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
+-----   - - - - - - - - -
+   . . . . XXX . . .
+   . . . . XXXXX . .
+   . . XXX XXXXX . .
+   . . XXX XXXXXXX .
+   . XXXXX XXXXXXX .
+   . XXXXX XXXXXXXXX
+   . XXXXXXXXXXXXXXX
+   XXXXXXXXXXXXXXXXX
+Measures of cluster adequacy
+      2      3      4      5      6      7
+             ------ ------ ------ ------ ------ ------ ------
+     Eta  -0.284 -0.480 -0.554 -0.657 -0.711 -0.687 -0.151
+       Q  -0.133 -0.163 -0.188 -0.203 -0.240 -0.214 -0.033
+Q-prime  -0.152 -0.190 -0.226 -0.254 -0.320 -0.322 -0.065
+     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
+     2     3     4     5     6     7     8
+         ----- ----- ----- ----- ----- ----- ----- -----
+CL1  0.222 0.333 0.333 0.111 0.111 0.111 0.111 1.000
+CL2  0.111 0.111 0.111 0.444 0.444 0.333 0.889
+CL3  0.111 0.111 0.111 0.111 0.333 0.556
+CL4  0.111 0.111 0.111 0.222 0.111
+CL5  0.111 0.111 0.222 0.111
+CL6  0.111 0.111 0.111
+CL7  0.111 0.111
+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
+</code>
+{{hiclus2.gif}}
+{{hiclus4.gif}}
+====== E.g. 1 ======
+<code csv cities2.csv>0	206	429	1504	963	2976	3095	2979	1949
+	0	233	1308	802	2815	2934	2786	1771
+	233	0	1075	671	2684	2799	2631	1616
+	1308	1075	0	1329	3273	3053	2687	2037
+	802	671	1329	0	2013	2142	2054	996
+	2815	2684	3273	2013	0	808	1131	1307
+	2934	2799	3053	2142	808	0	379	1235
+	2786	2631	2687	2054	1131	379	0	1059
+	1771	1616	2037	996	1307	1235	1059	0
+</code>
+# Prepare Data
+setwd("d:/rdata")
+mydata <- read.csv("cities.csv")
+mydata <- na.omit(mydata) # listwise deletion of missing
+mydata <- scale(mydata) # standardize variables