Hierarchical clustering is sometimes used to generate K clusters, K > 1 by taking the clusters...
Hierarchical clustering is sometimes used to generate K clusters, K > 1 by taking the clusters at the Kth level of the dendrogram. (Root is at level 1.) By looking at the clusters produced in this way, we can evaluate the behavior of hierarchical clustering on different types of data and clusters, and also compare hierarchical approaches to K-means. The following is a set of one-dimensional points: {6, 12, 18, 24, 30, 42, 48}.
(a) For each of the following sets of initial centroids, create
two clusters by assigning each point to the nearest centroid, and
then calculate the total squared error for each set of two
clusters. Show both the clusters and the total squared error for
each set of centroids.
i. {18, 45}
ii. {15, 40}
(b) Do both sets of centroids represent stable solutions; i.e., if
the K-means algorithm was run on this set of points using the given
centroids as the starting centroids, would there be any change in
the clusters generated?
(c) What are the two clusters produced by single link?
Homework Answers
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Hierarchical clustering is sometimes used to generate K
clusters, K > 1 by taking the clusters...
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance Fa...
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
Business Analytics, Assignment on Clustering As part of the quarterly reviews, the manager of a r...
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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.,...
K-means clustering K-means clustering is a very well-known method of clustering unlabeled data. The simplicity of...
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For the following sets of two-dimensional points, (1) provide a sketch of how they would be split...
For the following sets of two-dimensional points, (1) provide a
sketch of how they would be split into clusters by K-means for the
given number of clusters and (2) indicate approximately where the
resulting centroids would be. Assume that we are using the squared
error objective function and random initialization of centroids. If
you think that there is more than one possible solution, then
please indicate whether each solution is a global or local minimum.
Darker areas indicate higher density....
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they wo...
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they would be split into clusters by K-means for the given number of clusters and (2) indicate approximately where the resulting centroids would be. Assume that we are using the squared error objective function. If you think that there is more than one possible solution, then please indicate whether each solution is a global or local minimum. Note that the label of each diagram...
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they wo...
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they would be split into clusters by K-means for the given number of clusters and (2) indicate approximately where the resulting centroids would be. Assume that we are using the squared error objective function. If you think that there is more than one possible solution, then please indicate whether each solution is a global or local minimum. Note that the label of each diagram...
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...
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Data clustering and the k means algorithm. However, I'm not able to list all of the...
Data clustering and the k means algorithm. However, I'm
not able to list all of the data sets but they include: ecoli.txt,
glass.txt, ionoshpere.txt, iris_bezdek.txt, landsat.txt,
letter_recognition.txt, segmentation.txt vehicle.txt, wine.txt and
yeast.txt.
Input: Your program should be non-interactive (that is, the program should not interact with the user by asking him/her explicit questions) and take the following command-line arguments: <F<K><I><T> <R>, where F: name of the data file K: number of clusters (positive integer greater than one) I: maximum number...
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
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.,...
For the following sets of two-dimensional points, (1) provide a
sketch of how they would be split into clusters by K-means for the
given number of clusters and (2) indicate approximately where the
resulting centroids would be. Assume that we are using the squared
error objective function and random initialization of centroids. If
you think that there is more than one possible solution, then
please indicate whether each solution is a global or local minimum.
Darker areas indicate higher density....
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they would be split into clusters by K-means for the given number of clusters and (2) indicate approximately where the resulting centroids would be. Assume that we are using the squared error objective function. If you think that there is more than one possible solution, then please indicate whether each solution is a global or local minimum. Note that the label of each diagram...
1. [10 points) For the following sets of two-dimensional points, (1) draw a sketch of how they would be split into clusters by K-means for the given number of clusters and (2) indicate approximately where the resulting centroids would be. Assume that we are using the squared error objective function. If you think that there is more than one possible solution, then please indicate whether each solution is a global or local minimum. Note that the label of each diagram...
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...
Data clustering and the k means algorithm. However, I'm
not able to list all of the data sets but they include: ecoli.txt,
glass.txt, ionoshpere.txt, iris_bezdek.txt, landsat.txt,
letter_recognition.txt, segmentation.txt vehicle.txt, wine.txt and
yeast.txt.
Input: Your program should be non-interactive (that is, the program should not interact with the user by asking him/her explicit questions) and take the following command-line arguments: <F<K><I><T> <R>, where F: name of the data file K: number of clusters (positive integer greater than one) I: maximum number...
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