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. | ||||||
|
|||||||
| (b) | Repeat the k-means clustering for values of: | ||||||
| k = 2 | |||||||
|
|||||||
| k = 4 | |||||||
|
|||||||
| k = 5 | |||||||
|
|||||||
| (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 |
Homework Answers
| (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. |
||||||
|
|||||||
| (b) | Repeat the k-means clustering for values of: |
||||||
| k = 2 | |||||||
|
|||||||
| k = 4 | |||||||
|
|||||||
| k = 5 | |||||||
|
|||||||
| (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 Answer to:
Question: Use the data file DemoKTC file to conduct the
following analysis.
(a)
Use k-means clustering...
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....
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...
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...
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...
Question 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....
Question 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
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.,...
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...
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 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...
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...
Most questions answered within 3 hours.
-
Costing Models You work for XYZ trucking and your boss has asked
you to figure out...
asked 1 minute ago -
International ethics focus on:
1) exploitation of labor
2)corruption
3) environmental impact
4) none of the...
asked 8 minutes ago -
A 24.6 g sample of brass is heated in a flask to 125°C in
boiling water...
asked 9 minutes ago -
1. When balancing the equation H2SO4 (aq) + NaOH (aq) →
Na2SO4(aq) + H2O(aq), would you...
asked 16 minutes ago -
Which drug candidates are most effective at docking with COX1
and COX2 respectively?
COX1: flurbiprofen, COX2:...
asked 22 minutes ago -
You place the left end of the rod on a fulcrum, and hang the
right end...
asked 24 minutes ago -
Push an object with half the mass of the cabinet with a force of
200 N....
asked 30 minutes ago -
Differentiate the application and functions of
electronic medical records, electronic health records and
electronic personal health...
asked 31 minutes ago -
What is the height of the tsunami in a water depth of 10 m
assuming the...
asked 42 minutes ago -
A χ2 goodness-of-fit test was used to test the hypothesis that
students at a local university...
asked 45 minutes ago -
Problem 1
You have a loan outstanding. It requires making three annual
payments at the end...
asked 47 minutes ago -
A database imposes several requirements on the structure of
tables. Which of the following is not...
asked 46 minutes ago