[amp_mcq option1=”The choice of an appropriate metric will influence the shape of the clusters” option2=”Hierarchical clustering is also called HCA” option3=”In general, the merges and splits are determined in a greedy manner” option4=”All of the mentioned” correct=”option4″]<\/p>\n
The correct answer is D. All of the mentioned.<\/p>\n
A. The choice of an appropriate metric will influence the shape of the clusters.<\/p>\n
This is because the metric determines how similar or dissimilar two data points are. For example, if the metric is Euclidean distance, then clusters will tend to be spherical in shape. If the metric is Manhattan distance, then clusters will tend to be rectangular in shape.<\/p>\n
B. Hierarchical clustering is also called HCA.<\/p>\n
HCA stands for Hierarchical Clustering Analysis. It is a method of cluster analysis that groups data points into clusters based on their similarity.<\/p>\n
C. In general, the merges and splits are determined in a greedy manner.<\/p>\n
In greedy clustering, the algorithm always chooses the best option at each step, without considering the future consequences of its choices. This can lead to suboptimal results, but it is often much faster than other methods.<\/p>\n
In conclusion, all of the statements are correct.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”The choice of an appropriate metric will influence the shape of the clusters” option2=”Hierarchical clustering is also called HCA” option3=”In general, the merges and splits are determined in a greedy manner” option4=”All of the mentioned” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-49148","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n