Thanks for the wonderful video, Hannah! This is great material. I am preparing for a risk certification, and this really helped me revise my concepts in a much better way. Have a great day!
Pls endeavour to avoid making mistakes thanks for comment section i could have got it so difficult to comprehend. That aspect of sqrt of 17 is terrible. But u did well and this video is good too
The Euclidean distance horizontal component at 2:17 should be 3 not 4 since 4 - 1 = 3. Also, the manhattan distance should be 4 and the maximum distance should be 3 for the same reason.
Hi many thanks for your question, Hierarchical Cluster Analysis (HCA) is not always associated with the Euclidean distance. While Euclidean distance is commonly used, HCA can work with various distance metrics depending on the nature of the data and the analysis goals. Here are some common distance metrics used in HCA: - Euclidean Distance: This is the straight-line distance between two points in a multi-dimensional space. It's one of the simplest and most widely used distance metrics. - Manhattan Distance (also known as City Block or L1 distance): This is the sum of absolute differences between coordinates. It can be suitable when diagonal movement isn't meaningful. - Cosine Similarity: This measures the cosine of the angle between two vectors, commonly used in text analysis and other contexts where vector magnitude might vary. - Mahalanobis Distance: It accounts for correlations in data by incorporating the covariance matrix, making it suitable for data with different scales and correlations among variables. - Minkowski Distance: A generalization of Euclidean and Manhattan distances, with a parameter 'p' to control the degree of the norm. - Correlation-based Distance: This distance uses the correlation between data points rather than absolute differences. It's common in gene expression analysis or other contexts where relationships between variables matter more than absolute values. I hope this was helpful : ) Regards Hannah
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