k-Means Cluster Analysis | Multivariate Analysis | Past 4.16c
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Welcome to our tutorial series
Welcome to our tutorial series on Past 4.16c! In this video, we'll delve into k-Means Cluster Analysis, a powerful technique for grouping data points into clusters based on similarity. Join us as we explore how to perform k-Means Cluster Analysis in Past 4.16c and unlock valuable insights from your datasets.
K-means clustering is a popular unsupervised machine learning algorithm used for clustering data points into a pre-defined number of clusters based on their features.
WGSS - Within-Group Sum of Squares
A lower value of WGSS indicates that data points within each cluster are closer to their respective centroids, implying tighter or more compact clusters.
Conversely, a higher value of WGSS suggests greater variability or dispersion within clusters, indicating less distinct or more spread-out clusters.
F-statistic or F-ratio
A higher value of the F-statistic indicates greater separation or discrimination between clusters relative to the dispersion of data points within clusters.
It suggests that the clustering solution effectively captures the differences between clusters and forms distinct and well-separated clusters.
Percentage of Variance Explained (Var %)
The percentage of variance explained by each principal component (Var %) quantifies the proportion of total variance in the dataset accounted for by that particular component.
A higher percentage of variance explained by a principal component indicates that the component captures a larger amount of variation in the original data.
Principal components with higher Var % values are considered more informative and contribute more significantly to the overall structure of the data.
Average Silhouette Width (Av. Silh):
"Av. Silh" likely refers to the average silhouette width, which is a metric used to evaluate the quality of clustering solutions.
The silhouette width is a measure of how well each data point fits its assigned cluster and how well-separated the clusters are from each other.
The average silhouette width (Av. Silh) is the average of the silhouette widths of all data points across all clusters.
The silhouette width ranges from -1 to 1. A value close to 1 indicates that the data point is well-clustered and lies far from neighboring clusters, while a value close to -1 indicates that the data point may have been assigned to the wrong cluster.
The average silhouette width provides an overall measure of the quality and coherence of the clustering solution. Higher values of Av. Silh indicate better-defined and more distinct clusters.
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Software Version : Past 4.16c (Freeware)
Disclaimer
This video is made for the sole purpose of higher education. Care is taken to provide the most accurate information. However, we can’t guarantee the accuracy of all the information in this video. Kindly do your own research before coming to any conclusions or making any decisions.
📌 Tags:
#biostatistics #statistics #dataanalysis #statisticalanalysis
#datavisualization #datascience #dataanalytics #datamining #clusteranalysis #k-means #multivariate
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3 ماه پیش
در تاریخ 1403/03/16 منتشر شده
است.
350
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