Question

In Problem 2, you found a normalized distance matrix between Berkeley, Cal Tech, UCLA, and UNC for the Excel file Colleges and Universities Cluster Analysis Worksheet. Apply single linkage clustering to these schools and draw a dendogram illustrating the clustering process.Euclidean Distances between schools (answer to problem 2)Berkeley Cal Tech 2.61613262Berkeley UCLA 0.27049639Berkeley UNC 0.51858037Cal Tech UCLA 2.73391121Cal Tech UNC 3.04035773UCLA UNC 0.3093713

Answer 1

The Adjacent Entries Distance between the Self Functional Maps is used to generate the distance matrix between the forms, DR+NN, where N is the total number of shapes in the benchmark (94)Dij=DAE(Ci,Cj)i,j1...N.

The Euclidean distance, also known as Euclidean space, is the shortest distance between any two points in an N-dimensional space. It is employed in a variety of domains, including geometry, data mining, deep learning, and others, as a common metric to assess the similarity between two data points. A table that displays the separation between two objects is referred to as a distance matrix. For instance, the table below shows that there are 16 miles between points A and B, 47 miles between A and C, and so on. Distance is the definition of an object.

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