Question

Question: Use the data file DemoKTC file to conduct the following analysis. (a) Use k-means clustering...

Question: Use the data file DemoKTC file to conduct the following analysis.

(a) Use k-means clustering with a value of k = 3 to cluster based on the Age, Income, and Children variables to reproduce the results in Appendix 4.2.
Average distance within least dense cluster
Minimum cluster distance to least dense cluster
(b) Repeat the k-means clustering for values of:
k = 2
Average distance within least dense cluster
Minimum cluster distance to least dense cluster
k = 4
Average distance within least dense cluster
Minimum cluster distance to least dense cluster
k = 5
Average distance within least dense cluster
Minimum cluster distance to least dense cluster
(c) How many clusters do you recommend? Why?
Age Female Income Married Children Car Loan Mortgage
48 1 17546.00 0 1 0 0
40 0 30085.10 1 3 1 1
51 1 16575.40 1 0 1 0
23 1 20375.40 1 3 0 0
57 1 50576.30 1 0 0 0
57 1 37869.60 1 2 0 0
22 0 8877.07 0 0 0 0
58 0 24946.60 1 0 1 0
37 1 25304.30 1 2 1 0
54 0 24212.10 1 2 1 0
66 1 59803.90 1 0 0 0
52 1 26658.80 0 0 1 1
44 1 15735.80 1 1 0 1
66 1 55204.70 1 1 1 1
36 0 19474.60 1 0 0 1
38 1 22342.10 1 0 1 1
37 1 17729.80 1 2 0 1
46 1 41016.00 1 0 0 1
62 1 26909.20 1 0 0 0
31 0 22522.80 1 0 1 0
61 0 57880.70 1 2 0 0
50 0 16497.30 1 2 0 0
54 0 38446.60 1 0 0 0
27 1 15538.80 0 0 1 1
22 0 12640.30 0 2 1 0
56 0 41034.00 1 0 1 1
45 0 20809.70 1 0 0 1
39 1 20114.00 1 1 0 0
39 1 29359.10 0 3 1 1
61 0 24270.10 1 1 0 0
0 0
Add a comment Improve this question Transcribed image text
Answer #1
(a) Use k-means clustering
with a value of k = 3 to cluster based on the Age, Income,
and Children variables to reproduce the results in Appendix
4.2.
Average distance within least dense
cluster
Minimum cluster distance to least
dense cluster
(b) Repeat the k-means clustering for
values of:
k = 2
Average distance within least dense
cluster
Minimum cluster distance to least
dense cluster
k = 4
Average distance within least dense
cluster
Minimum cluster distance to least
dense cluster
k = 5
Average distance within least dense
cluster
Minimum cluster distance to least
dense cluster
(c) How many clusters do you recommend?
Why?
The input in the box below will not
be graded, but may be reviewed and considered by your
instructor.
Add a comment
Know the answer?
Add Answer to:
Question: Use the data file DemoKTC file to conduct the following analysis. (a) Use k-means clustering...
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for? Ask your own homework help question. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • K-means clustering K-means clustering is a very well-known method of clustering unlabeled data. The simplicity of...

    K-means clustering K-means clustering is a very well-known method of clustering unlabeled data. The simplicity of the process made it popular to data analysts. The task is to form clusters of similar data objects (points, properties etc.). When the dataset given is unlabeled, we try to make some conclusion about the data by forming clusters. Now, the number of clusters can be pre-determined and number of points can have any range. The main idea behind the process is finding nearest...

  • Please write full justification for (a) and (b). Will uprate/vote! 4. K-means The goal of K-means clustering is to divide a set of n points into k< n subgroups of points that are "close" t...

    Please write full justification for (a) and (b). Will uprate/vote! 4. K-means The goal of K-means clustering is to divide a set of n points into k< n subgroups of points that are "close" to each other. Each subgroup (or cluster) is identified by the center of the cluster, the centroid (μι, μ2' ··· ,14k) In class, we have seen a brute force approach to solve this problem exactly. Each of the k clusters is represented by a color, e.g.,...

  • Business Analytics, Assignment on Clustering As part of the quarterly reviews, the manager of a r...

    Business Analytics, Assignment on Clustering As part of the quarterly reviews, the manager of a retail store analyzes the quality of customer service based on the periodic customer satisfaction ratings (on a scale of 1 to 10 with 1 = Poor and 10 = Excellent). To understand the level of service quality, which includes the waiting times of the customers in the checkout section, he collected data on 100 customers who visited the store; see the attached Excel file: ServiceQuality....

  • 1. apply k-means clustering to a dataset Task Consider the following set of two-dimensional records: RID...

    1. apply k-means clustering to a dataset Task Consider the following set of two-dimensional records: RID Dimension 1 Dimension2 1 00 8 4 5 4 N 3 2 4 4 6 N 5 2. 00 6 00 8 6 Use the k-means algorithm to cluster the data in the dataset with K=3. You can assume that the records with RIDS 1, 3, and 5 are used for the initial cluster centroids (means). You must include the intermediate results in each...

  • K-means clustering Problem 1. (10 pts) Suppose that we have the gene expression values for 5...

    K-means clustering Problem 1. (10 pts) Suppose that we have the gene expression values for 5 genes (G1 to G5) under 4 time points (t1 to t4) as shown in the following table. Please use K-Means clustering to group 5 genes into 2 clusters based on Euclidean distance. Find out the final centroids and their affiliated genes. The initial centroids are c1=(1,2,3,4) and c2=c(9,8,7,6). Please write down your algorithm step by step. Result without steps won't get points. t1 t2...

  • In C++ program a simple k-means clustering algorithm, kmeans, using the Euclidean distance for 2-dimensional numerical...

    In C++ program a simple k-means clustering algorithm, kmeans, using the Euclidean distance for 2-dimensional numerical data. Your program should be executed as follows: kmeans k input.txt where input parameter k > 1 is an integer, specifying the number of clusters. input.txt is an input file containing many 2-dimensional data points in the following format, 274 119 317 144 267 164 233 137 272 99 297 116 268 142 522 286 468 308 441 263 Your program should output a...

  • Gi 1D: 11-10,22 = 8,13-6,24 clustering to obtain k 2 clusters by hand. Specifically, 4,15-3,16 2,...

    Gi 1D: 11-10,22 = 8,13-6,24 clustering to obtain k 2 clusters by hand. Specifically, 4,15-3,16 2, perform k-means ven the following six iteims in 1. Start from initial cluster centers c0,2 9. Show your steps for all iterations: (1 the cluster assignments i.... ys: (2) the updated cluster centers at the end of that iteration; (3) the energy at the end of that iteration 2. Repeat the above but start from initial cluster centers c 3. Which k-means solution is...

  • Question 4 1 pts Which of the following reasons is not the reason why the K-means...

    Question 4 1 pts Which of the following reasons is not the reason why the K-means algorithm will likely end up with sub-optimal clustering? (Select all that apply.) Bad choices for the initial cluster centers. Choosing a k that corresponds to the number of natural clusters in the dataset. Fast convergence of the K-means algorithm. Existence of closely located data samples in the dataset. Question 5 1 pts Which of the following is a step in K-means algorithm implementation? (Select...

  • Given the following data points, use the K-Means algorithm to cluster them into 2 clusters. Use...

    Given the following data points, use the K-Means algorithm to cluster them into 2 clusters. Use (31,32) as the centroid of the first cluster and (34,24) as the centroid of the second cluster. Show your calculations and the final clusters. 1 2 3 4 5 6 7 8 9 10 x 11 11 15 20 25 26 31 34 40 43 y 6 38 18 40 24 8 32 24 41 47

  • 1. Implement the K-means algorithm using these two as a reference. 2.Use Matlab’s implementation of kmeans...

    1. Implement the K-means algorithm using these two as a reference. 2.Use Matlab’s implementation of kmeans to check your results on the fisheriris dataset (https://www.mathworks.com/help/stats/kmeans.html) a. The fisheriris dataset is built into Matlab, and you can load it using ‘load fisheriris’. b. Please note the labels are available for the dataset, so you can check the performance of the kmeans algorithm on the dataset. 274 14 Unsupervised Lnn Fig. 14.1 A two-dimensional domain with clusters of examples weight bot initial...

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT