What ratio indicates the strength of a cluster? Between-cluster distance to average within-cluster distance The most similar observations to the least similar observations between two clusters Cluster centroids to the most similar observations between two clusters Cluster euclidean distance to cluster mean

What ratio indicates the strength of a cluster? Between-cluster distance to average within-cluster distance The most similar observations to the least similar observations between two clusters Cluster centroids to the most similar observations between two clusters Cluster euclidean distance to cluster mean

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter20: Queuing Theory

Section20.2: Modeling Arrival And Service Processes

Problem 1P

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You would like to conduct a cluster analysis with three binary variables. For all three of these variables, you can be sure that all of the observations coded "1" are similar to each other, and the observations coded "0" are similar to each other. Which distance measure should you use? Question 24 options: Matching distance Manhattan distance Jaccard's distance Euclidean distance
A cluster as the results of a cluster analysis cannot be save as a dimension. Group of answer choices True False
Which statement best describes k-means cluster analysis? Question 9 options: It is the process of organizing observations into distinct groups based on a measure of similarity or dissimilarity. It is the process of agglomerating observations into a series of nested groups based on a measure of similarity or dissimilarity. It is the process of reducing the number of variables to consider in data-mining. It is the process of estimating the value of a continuous outcome variable.
If u kw 100% thn Answer if any got plagrism contents quickly i will down vote Cse@ Cluster Analysis Algorithms Discuss how number of clusters can be decreased by dispersing a cluster and combining two clusters
Please please do this manually a.What is the distance between the two farthest members? (max or complete link) (round to four decimal places here, and next 2 problems); b. What is the distance between the two closest members? (min or single link);c. What is the average distance between all pairs? d. What is the center distance between two clusters? e. Among all three distances above, which one is robust to noise? Answer either “complete”, “single”, “average”, and "center"
Question No. 3: a) Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. b) Given are the 1-dimensional points A = 2, B = 3, C = 4, D = 9, E = 10, F = 11. Compute complete-linkage hierarchical clustering using d (x, y) = |x – y| as the distance between points. And also draw a dendrogram of it.
In k-means cluster analysis (data miner), how can you select the number of segments in the market?
You created a model using k-means. You made a graph of a metric that you are using to determine the best number of clusters. Which option is a reasonable choice as a metric to use? Group of answer choices Sum of Square Error Precision Gaussian Difference Sigmoid
Based on the following Dendrogram, if we change the distance from 3.4 to 4.4, which one is true? Group of answer choices number of clusters does not change. number of clusters changes from 4 to 6. number of clusters changes from 6 to 4. number of clusters changes from 7 to 3
How do you do Cluster Analysis if you have (1) nominal data and (2) ordinal data?
Logistic regression cannot correctly discriminate a set of data points that cannot be separated by a single hyperplane? True False Which of the following regarding medoid is NOT true? A medoid is the most centrally located object in a cluster A medoid is constructed by taking the mean value of the objects in a cluster A medoid in a cluster is a real instance
The procedure of evaluating the results of a clustering algorithm is known under the termcluster validity. In general terms, there are two approaches to investigate cluster validity Internal and External criteria. Both DB (Davies-Bouldin) and Silhouette Coefficient are internal criteria. Which one is NOT correct about these two criteria? Group of answer choices The minimum DB score is zero, with lower values indicating better clustering. DB is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The best value of Silhouette is 1 and the worst value is -1 No answer text provided.