R Data Analysis Cookbook by Viswanathan Viswa & Viswanathan Shanthi

Author:Viswanathan, Viswa & Viswanathan, Shanthi
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2015-05-28T16:00:00+00:00

How it works...

In step 1 the data is read and in step 2 we define the convenience function for scaling a set of variables in a data frame.

In step 3 the convenience function is used to scale only the variables of interest. We leave out the No, model_year, and car_name variables.

In step 4 the distance matrix is created based on the standardized values of the relevant variables. We have computed Euclidean distances; other possibilities are: maximum, manhattan, canberra, binary, and minkowski.

In step 5 the distance matrix is passed to the hclust function to create the clustering model. We specified method = "ward" to use Ward's method, which tries to get compact spherical clusters. The hclust function also supports single, complete, average, mcquitty, median, and centroid.

In step 6 the resulting dendrogram is plotted. We specified labels=FALSE because we have too many cases and printing them will only add clutter. With a smaller dataset, using labels = TRUE will make sense. The hang argument controls the distance from the bottom of the dendrogram to the labels. Since we are not using labels, we specified hang = 0 to prevent numerous vertical lines below the dendrogram.

The dendrogram shows all the cases at the bottom (too numerous to distinguish in our plot) and shows the step-by-step agglomeration of the clusters. The dendrogram is organized in such a way that we can obtain a desired set of clusters, say K, by drawing a horizontal line in such a way that it cuts across exactly K vertical lines on the dendrogram.

Step 7 show how to use the rect.hclust function to demarcate the cases comprising the various clusters for a selected value of k.

Step 8 shows how we can use the cutree function to identify, for a specific K, which cluster each case of our data belongs to.

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