Coped from Task:K-Means and Mini-Batch K-Means Ind #5
Below are my evaluations of the plots for using kmeans on the individual dataset.
The Cluster Centers plot shows the average values of each feature for the three identified clusters. Each bar represents a feature, and its height reflects the mean value of that feature within the cluster. The features include demographic, socio-economic, and health-related variables. Notably, variables such as AINCTOT (total income), AFTOTINC (family total income), and POVERTY show significant differences across clusters, indicating that income and poverty status are key distinguishing factors among the clusters.
The silhouette score is 0.869, which is quite high. A high silhouette score close to 1 indicates that the clusters are well-defined and distinct from each other. The points within each cluster are very similar to each other and different from points in other clusters. The clustering model has successfully grouped the data points into clusters that have high internal similarity and low external similarity.
Based on the analysis and visualizations, KMeans appears to be a good model for clustering this dataset. The high silhouette score of 0.869 indicates that the clusters are well-defined and distinct from each other, with data points closer to their assigned cluster centers compared to other clusters. The PCA plot further supports this, showing clear separation between clusters in a two-dimensional space. The cluster centers plot reveals meaningful differences across several features, particularly income-related variables. Together, these findings suggest that KMeans effectively captures the underlying structure of the data, making it a suitable model for identifying and analyzing patterns within the dataset.
Coped from Task:K-Means and Mini-Batch K-Means Ind #5
Below are my evaluations of the plots for using kmeans on the individual dataset.
The Cluster Centers plot shows the average values of each feature for the three identified clusters. Each bar represents a feature, and its height reflects the mean value of that feature within the cluster. The features include demographic, socio-economic, and health-related variables. Notably, variables such as AINCTOT (total income), AFTOTINC (family total income), and POVERTY show significant differences across clusters, indicating that income and poverty status are key distinguishing factors among the clusters.
The silhouette score is 0.869, which is quite high. A high silhouette score close to 1 indicates that the clusters are well-defined and distinct from each other. The points within each cluster are very similar to each other and different from points in other clusters. The clustering model has successfully grouped the data points into clusters that have high internal similarity and low external similarity.
Based on the analysis and visualizations, KMeans appears to be a good model for clustering this dataset. The high silhouette score of 0.869 indicates that the clusters are well-defined and distinct from each other, with data points closer to their assigned cluster centers compared to other clusters. The PCA plot further supports this, showing clear separation between clusters in a two-dimensional space. The cluster centers plot reveals meaningful differences across several features, particularly income-related variables. Together, these findings suggest that KMeans effectively captures the underlying structure of the data, making it a suitable model for identifying and analyzing patterns within the dataset.