ISYE6501 Week 2 HW Latest 2022

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ISYE6501 Week 2 HW Question 4.1 At my job we use clustering in an image system to understand the minerals that are coming into our processing facility. Comparing the images to known mineral images, ... the system can quantify the mineral content and monitor the colors of the minerals to compare to centroids of the clusters. Question 4.2 library(kernlab) library(ggplot2) ## Warning: package 'ggplot2' was built under R version 3.6.3 ## ## Attaching package: 'ggplot2' ## The following object is masked from 'package:kernlab': ## ## alpha library(kknn) ## Warning: package 'kknn' was built under R version 3.6.3 data<-read.table("C:\\Users\\Colin Shumaker\\Desktop\\ISYE6501\\Week 2\\data 4.2\\iri s.txt", header = TRUE) data1 <-data[,1:4] head(data1) ## Sepal.Length Sepal.Width Petal.Length Petal.Width ## 1 5.1 3.5 1.4 0.2 ## 2 4.9 3.0 1.4 0.2 ## 3 4.7 3.2 1.3 0.2 ## 4 4.6 3.1 1.5 0.2 ## 5 5.0 3.6 1.4 0.2 ## 6 5.4 3.9 1.7 0.4 species <-data[,5] kvalues <- rep(0,10) kx <- 1:10 set.seed(1000) for (r in 1:10){ ktest <- kmeans(data1,r,nstart=5,iter.max = 10) kx[r]<-r kvalues[r]<-ktest$tot.withins } plot(kx,kvalues,type="b",xlab="Number of Clusters", ylab="Total Distance") set.seed(4) k <- kmeans(data1,3) cat("Predictors 1,2,3,4","\n") ## Predictors 1,2,3,4 table(k$cluster,species) ## species ## setosa versicolor virginica ## 1 0 48 14 ## 2 0 2 36 ## 3 50 0 0 set.seed(5) k1 <- kmeans(data1[,1:3],3) cat("Predictors 1,2,3","\n") ## Predictors 1,2,3 table(k1$cluster,species) ## species ## setosa versicolor virginica ## 1 0 45 13 ## 2 50 0 0 ## 3 0 5 37 set.seed(6) k2 <- kmeans(data1[,2:4],3) cat("Predictors 2,3,4","\n") ## Predictors 2,3,4 table(k2$cluster,species) ## species ## setosa versicolor virginica [Show More]

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