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1.3 1.9 2.2 1 4.2 2 1.9 1.9 the “Euclidean” metric. The symbol z in the matrix below is to be calculated later. A B C D E F G H A 0 3.375 4.174 2.322 4.7 4.272 3.09 4.091 B 3.375 0 5.205 4.628 5.69 4.93 1.581 5.606 C 4.174 5.205 0 4.298 x 3.848 4.021 4.95 D 2.322 4.628 4.298 0 4.207 4.602 4.27 E 4.7 5.69 I 5.4 0 F 4.272 4.93 3.848 4.207 2.186 0 2.186 5.113 1.769 4.589 1.977 5.356 G 4.602 5.113 4.589 0 3.09 1.581 4.021 4.091 5.606 4.95 4.27 1.769 1.977 5.356 0 H I 4.027 3.419 5.365 4.965 3.012 2.766 3.626 3.053 hissing distance x. Compute its value and write it. H I 5.4 H and ABCDEFI. Compute and write the dissimilarity between these clusters under "average" linka mand KM<-kmeans(x=x, centers=3) was run, with the following output center of the cluster identified with the label 1. By computing this center manually or otherwise, i ed by this cluster analysis. command pam (x=X, k=3)->PM was run, with the following output:

1.3 1.9 2.2 1 4.2 2 1.9 1.9 the “Euclidean” metric. The symbol z in the matrix below is to be calculated later. A B C D E F G H A 0 3.375 4.174 2.322 4.7 4.272 3.09 4.091 B 3.375 0 5.205 4.628 5.69 4.93 1.581 5.606 C 4.174 5.205 0 4.298 x 3.848 4.021 4.95 D 2.322 4.628 4.298 0 4.207 4.602 4.27 E 4.7 5.69 I 5.4 0 F 4.272 4.93 3.848 4.207 2.186 0 2.186 5.113 1.769 4.589 1.977 5.356 G 4.602 5.113 4.589 0 3.09 1.581 4.021 4.091 5.606 4.95 4.27 1.769 1.977 5.356 0 H I 4.027 3.419 5.365 4.965 3.012 2.766 3.626 3.053 hissing distance x. Compute its value and write it. H I 5.4 H and ABCDEFI. Compute and write the dissimilarity between these clusters under "average" linka mand KM<-kmeans(x=x, centers=3) was run, with the following output center of the cluster identified with the label 1. By computing this center manually or otherwise, i ed by this cluster analysis. command pam (x=X, k=3)->PM was run, with the following output:

Question
Consider the following data set with n = 9 observations and p = 4 variables. The data set is given next V1 V2 V3 V4 A 1.9 1.3 3.1 4.9 3.2 4.9 B 5.2 2 C 1.3 5.2 4.2 4.1 D 1.2 1 5.1 4 E 1.7 3.3 1.2 1.1 F 2.3 3.2 3.3 1.1 G 4.3 3.2 2.8 5.2 H 1.3 1.9 2.2 1 I 4.2 2 1.9 1.9 as well as the distance matrix using the "Euclidean” metric. The symbole in the matrix below is to be calculated later. F A B 4.298 B с D E G HI 3.375 4.174 2.322 4.7 4.272 3.09 4.091 4.027 3.375 0 5.205 4.628 5.69 4.93 1.581 5.606 3.419 C 4.174 5.205 0 4.298 x 3.848 4.021 4.95 5.365 D 2.322 4.628 0 5.4 4.207 4.602 4.27 4.965 E 4.7 5.69 5.4 0 2.186 5.113 1.769 3.012 F 4.272 4.93 3.848 4.207 2.186 0 4.589 1.977 2.766 G 3.09 1.581 4.021 4.602 5.113 4.589 0 5.356 3.626 H 4.091 5.606 4.95 4.27 1.769 1.977 5.356 0 3.053 4.027 3.419 5.365 4.965 3.012 2.766 3.626 3.053 0 I A 0 x A) In the distance matrix there is a missing distance x. Compute its value and write it. B) Consider two arbitrary clusters GH and ABCDEFI. Compute and write the dissimilarity between these clusters under "average" linkage. C) Using the above data X, the R command KM<- kmeans(x=X, centers=3) was run, with the following output > KM$cluster [1] 2, 1, 2, 2, 3, 3, 1, 3, 3 There is interest in determining the center of the cluster identified with the label 1. By computing this center manually or otherwise, identify which of the following is the correct centroid of this cluster: D) Still using the above data X, the R command pam (x=X, k=3) ->PM was run, with the following output: > PM$id.med [1] 1, 7, 6 Identify correctly the medoids yielded by this cluster analysis.
Transcribed Image Text:Consider the following data set with n = 9 observations and p = 4 variables. The data set is given next V1 V2 V3 V4 A 1.9 1.3 3.1 4.9 3.2 4.9 B 5.2 2 C 1.3 5.2 4.2 4.1 D 1.2 1 5.1 4 E 1.7 3.3 1.2 1.1 F 2.3 3.2 3.3 1.1 G 4.3 3.2 2.8 5.2 H 1.3 1.9 2.2 1 I 4.2 2 1.9 1.9 as well as the distance matrix using the "Euclidean” metric. The symbole in the matrix below is to be calculated later. F A B 4.298 B с D E G HI 3.375 4.174 2.322 4.7 4.272 3.09 4.091 4.027 3.375 0 5.205 4.628 5.69 4.93 1.581 5.606 3.419 C 4.174 5.205 0 4.298 x 3.848 4.021 4.95 5.365 D 2.322 4.628 0 5.4 4.207 4.602 4.27 4.965 E 4.7 5.69 5.4 0 2.186 5.113 1.769 3.012 F 4.272 4.93 3.848 4.207 2.186 0 4.589 1.977 2.766 G 3.09 1.581 4.021 4.602 5.113 4.589 0 5.356 3.626 H 4.091 5.606 4.95 4.27 1.769 1.977 5.356 0 3.053 4.027 3.419 5.365 4.965 3.012 2.766 3.626 3.053 0 I A 0 x A) In the distance matrix there is a missing distance x. Compute its value and write it. B) Consider two arbitrary clusters GH and ABCDEFI. Compute and write the dissimilarity between these clusters under "average" linkage. C) Using the above data X, the R command KM<- kmeans(x=X, centers=3) was run, with the following output > KM$cluster [1] 2, 1, 2, 2, 3, 3, 1, 3, 3 There is interest in determining the center of the cluster identified with the label 1. By computing this center manually or otherwise, identify which of the following is the correct centroid of this cluster: D) Still using the above data X, the R command pam (x=X, k=3) ->PM was run, with the following output: > PM$id.med [1] 1, 7, 6 Identify correctly the medoids yielded by this cluster analysis.
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