The dataset we are going to use has 1000 entries with unknown number of clusters (download data_Kclusters.txt from Blackboard). Use the Elbow Method to find the optimum value for K and show the results for clustering using the optimum k. You can use the following lines to plot the elbow. WCSS = [] K = range (1,10) for this k in K: kmeanModel KMeans (n_clusters = this_k) kmeanModel.fit(x) WCSS.append(kmeanModel.inertia_) #Plot the elbow plt.figure() plt.plot(K, WCSS, 'bx-') plt.xlabel('k') plt.ylabel('WCSS') plt.title('The Elbow Method showing the optimal k')
The dataset we are going to use has 1000 entries with unknown number of clusters (download data_Kclusters.txt from Blackboard). Use the Elbow Method to find the optimum value for K and show the results for clustering using the optimum k. You can use the following lines to plot the elbow. WCSS = [] K = range (1,10) for this k in K: kmeanModel KMeans (n_clusters = this_k) kmeanModel.fit(x) WCSS.append(kmeanModel.inertia_) #Plot the elbow plt.figure() plt.plot(K, WCSS, 'bx-') plt.xlabel('k') plt.ylabel('WCSS') plt.title('The Elbow Method showing the optimal k')
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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answer in python
![The dataset we are going to use has 1000 entries with unknown number of clusters (download
data Kclusters.txt from Blackboard). Use the Elbow Method to find the optimum value for K and show
the results for clustering using the optimum k. You can use the following lines to plot the elbow.
WCSS = []
K = range (1,10)
for this k in K:
kmeanModel KMeans (n_clusters = this_k)
kmeanModel.fit(x)
WCSS.append(kmeanModel.inertia_)
#Plot the elbow
plt. figure()
plt.plot(K, WCSS, 'bx-')
plt.xlabel('k')
plt.ylabel('WCSS')
plt.title('The Elbow Method showing the optimal k')](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0844382d-a160-4633-90ba-cc3fe036bd02%2F6be6e8f7-e909-42bd-ab68-bbfc330a5822%2Fbop1gqi_processed.jpeg&w=3840&q=75)
Transcribed Image Text:The dataset we are going to use has 1000 entries with unknown number of clusters (download
data Kclusters.txt from Blackboard). Use the Elbow Method to find the optimum value for K and show
the results for clustering using the optimum k. You can use the following lines to plot the elbow.
WCSS = []
K = range (1,10)
for this k in K:
kmeanModel KMeans (n_clusters = this_k)
kmeanModel.fit(x)
WCSS.append(kmeanModel.inertia_)
#Plot the elbow
plt. figure()
plt.plot(K, WCSS, 'bx-')
plt.xlabel('k')
plt.ylabel('WCSS')
plt.title('The Elbow Method showing the optimal k')
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