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ellipses for Cluster 1 and Cluster 2 close to that of Cluster 0? I

ellipses for Cluster 1 and Cluster 2 close to that of Cluster 0? I

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Is the direction of the major axes of the ellipses for Cluster 1 and Cluster 2 close to that of Cluster 0? If so, that indicates that the separation of the clusters in this projection is poor. Do you think three clusters is too many for this dataset?

 

### Fire Clustering Analysis **Experiment Title:** Fire Clustering - Keyaunta Colson **Platform:** Microsoft Azure Machine Learning Studio (Classic) **Graph Description:** The graph presents a visualization of clustering results obtained through a machine learning model trained on a dataset. The clusters are plotted using Principal Component Analysis (PCA), which reduces the dataset's dimensionality to two principal components for easier visualization. - **Axes:** - **X-Axis:** Principal Component 1 - **Y-Axis:** Principal Component 2 - **Clusters:** - **Cluster 0:** Represented in blue. - **Cluster 1:** Represented in red. - **Cluster 2:** Represented in green. Each color indicates a distinct cluster identified by the model. The ellipses surrounding each cluster suggest the variance and spread of the data points within the respective clusters on the PCA plot. This visualization helps in understanding how the data is naturally grouped and could provide insights into patterns or underlying structures within the dataset. It is a crucial step for tasks such as anomaly detection, data segmentation, or discovering natural groupings among data points.
Transcribed Image Text:### Fire Clustering Analysis **Experiment Title:** Fire Clustering - Keyaunta Colson **Platform:** Microsoft Azure Machine Learning Studio (Classic) **Graph Description:** The graph presents a visualization of clustering results obtained through a machine learning model trained on a dataset. The clusters are plotted using Principal Component Analysis (PCA), which reduces the dataset's dimensionality to two principal components for easier visualization. - **Axes:** - **X-Axis:** Principal Component 1 - **Y-Axis:** Principal Component 2 - **Clusters:** - **Cluster 0:** Represented in blue. - **Cluster 1:** Represented in red. - **Cluster 2:** Represented in green. Each color indicates a distinct cluster identified by the model. The ellipses surrounding each cluster suggest the variance and spread of the data points within the respective clusters on the PCA plot. This visualization helps in understanding how the data is naturally grouped and could provide insights into patterns or underlying structures within the dataset. It is a crucial step for tasks such as anomaly detection, data segmentation, or discovering natural groupings among data points.
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