[amp_mcq option1=”Continuous – euclidean distance” option2=”Continuous – correlation similarity” option3=”Binary – manhattan distance” option4=”None of the mentioned” correct=”option3″]<\/p>\n
The correct answer is C. Binary – Manhattan distance.<\/p>\n
Euclidean distance is a measure of the distance between two points in a Euclidean space. It is the length of the hypotenuse of a right triangle with sides corresponding to the differences between the coordinates of the two points.<\/p>\n
Correlation similarity is a measure of the similarity between two vectors. It is calculated by taking the dot product of the two vectors and normalizing it by the product of their norms.<\/p>\n
Manhattan distance is a measure of the distance between two points in a city block space. It is the sum of the absolute values of the differences between the coordinates of the two points.<\/p>\n
Binary data is data that can only take on two values, such as 0 or 1.<\/p>\n
The Manhattan distance is not a valid measure of distance for binary data because it does not take into account the order of the values. For example, the Manhattan distance between the points (0, 1) and (1, 0) is 2, even though the two points are actually the same.<\/p>\n
The other options are all valid combinations.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Continuous – euclidean distance” option2=”Continuous – correlation similarity” option3=”Binary – manhattan distance” option4=”None of the mentioned” correct=”option3″]<\/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-43887","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n