# Clustering # Author: Isabel Darcy # Date: Feb 5, 2017 a <- sort(runif(30, 0,3)) sa <-sin(a) plot(sa) b <- sort(runif(25, 0,3)) sb <-sin(b) plot(sb) c <- sort(runif(20, 0,3)) sc <- b^2 plot(sc) plot(ecdf(sa)) plot(ecdf(sb), add=TRUE, col="red") plot(ecdf(sc), add=TRUE, col="blue") plot(ecdf(sc)) plot(ecdf(sb), add=TRUE, col="red") plot(ecdf(sa), add=TRUE, col="blue") ks.test(sa, sb) ks.test(sc, sb) ks.test(sa, sc) ############################################################### ##### Create dendograms for a variety of data sets ##### ############################################################### ## Create dendograms for a variety of data sets including some of the data ## sets that you worked with earlier this week. Also create dendograms for ## data sets containing circles, noise, circles plus noise. See below for ## how to create these data sets. ## Recall that you can determine how to use a command using ? or help mydata <- Noise mydata <- c(0,7,2, 10, 6) d <- dist(as.matrix(mydata)) # find distance matrix for dataset hc <- hclust(d, method = "single") # apply hierarchical clustering plot(hc) # plot the dendrogram d ?hclust plot(as.dendrogram(hclust(dist(data)))) k <-3 #x <- identify(hclust(dist(data))) ?identify hc