Зміст курсу
Cluster Analysis in Python
Cluster Analysis in Python
Cluster Centers
In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.
For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.
If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.
Swipe to start coding
For the same dataset of points data compute the K-Medoids algorithm,
and display the cluster centers. Follow the next steps:
- Import
KMedoidsfunction fromsklearn_extra.clusterlibrary. - Create
KMedoidsmodelmodelwith 2 clusters. - Fit the
datato themodel. - Add
'prediction'column with predicted bymodellabels todata. - Use the
.cluster_centers_method to extract the clusters centers array. - Visualize the clusters (already done) and centers. Within the second
.scatterplotfunction sethueto10, andsto150.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Cluster Centers
In the previous chapter, we compared the K-Means and K-Medoids algorithms. It was mentioned that, unlike K-Means, the K-Medoids algorithm has data points as medoids. Let's visualize the differences.
For example, for the training dataset with three clusters we can visualize the clusters and their centers for both K-Means and K-Medoids. The results are displayed below.
If you watch closely to the right chart and compare it with the central one, you will notice that marked points are data points, unlike the points on the left chart. I've marked these points with green arrows. Let's represent the difference using a more obvious example! The result of clustering using the K-Means algorithm, and cluster centers (labeled with 10) are displayed below.
Swipe to start coding
For the same dataset of points data compute the K-Medoids algorithm,
and display the cluster centers. Follow the next steps:
- Import
KMedoidsfunction fromsklearn_extra.clusterlibrary. - Create
KMedoidsmodelmodelwith 2 clusters. - Fit the
datato themodel. - Add
'prediction'column with predicted bymodellabels todata. - Use the
.cluster_centers_method to extract the clusters centers array. - Visualize the clusters (already done) and centers. Within the second
.scatterplotfunction sethueto10, andsto150.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів