ISYE 6501
Introduction to Analytic Model
Homework 2 (Summer 2024)
Georgia Institute of Technology.
, lOMoARcPSD| 43283024
ISYE HW 2
HT
2024-05-27
Question 4.1 Describe a situation or problem from your job, everyday life, current events, etc., for which
a clustering model would be appropriate. List some (up to 5) predictors that you might use.
The primary difference between classification and clustering is the known versus unknown response. Clus-
tering could be used to identify patterns in a disease spreading in a population. For example, public health
officials could use clustering to identify hotspots, understand transmission dynamics, and allocate resources
more effectively. Clusters can also reveal groups of cases that share characteristics such as geographic lo-
cation, demographics, etc. Some predictors might include: 1. location 2. demographics 3. date of disease
onset 4. socioeconomic status 5. pre-existing conditions
Question 4.2 Use the R function kmeans to cluster the points as well as possible. Report the best combi-
nation of predictors, your suggested value of k, and how well your best clustering predicts flower type.
a. Finding number of k (clusters) First, we run the kmeans algorithm multiple times with different k-
values. Using an ‘elbow plot’ of the sum squared error, we find that k = 3 is a good number to start
with because this is the point where the reduction in SSE slows down significantly as k increases. This
‘elbow’ point suggests that adding more than 3 clusters doesn’t significantly improve the clustering.
iris_data <- read.table("week 2 data-summer/iris.txt", header=TRUE)
set.seed(123)
Total_Sum <- NULL
K <- (1:10)
for (i in K){
cl <- kmeans(iris_data[,-5],centers = i)
Total_Sum = c(Total_Sum, cl$tot.withinss)
}
plot(K, Total_Sum,type = "b")
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