Chapter 5

This exercise asks us to interpret and validate the consistency within our clusters of data. To do this, we will employ the silhouette index, which gives us a silhouette value measuring how similar an object is to its own cluster compared to other clusters. The silhouette index is as follows: $${\displaystyle S(i)=\frac{B(i)-A(i)}{ma{x}_i(A(i),B(i))}}$$ The book explains the equation by first defining that the average dissimilarity of a point $x_i$ to a cluster $C_k$ is the average of the distances from $x_i$ to all of the points in $C_k$.

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Chapter 5 covers Clustering Analysis for large scale data anlysis like DNA/RNA sequencing outputs. These methods produce so much data that more unbiased approaches are required when attempting to make correlations. unsupervised method A learning method where all variables are treated with the same status, rather than one variable being considered as an outcome or target. status A variable’s classification as an outcome/predictor (e.