Distance Measures Quiz (Easy)

What is the Euclidean distance between two points (x1, y1) and (x2, y2) in a two-dimensional space?

What is the Manhattan distance between two points (x1, y1) and (x2, y2) in a two-dimensional space?

For binary attributes, which distance measure is most appropriate?

What is the advantage of using cosine similarity over Euclidean distance for text data in clustering?

What is the formula for Jaccard distance for two sets A and B?

In clustering, the Manhattan distance is also known as:

What does the cosine similarity measure?

Which distance measure is invariant to scale and translation? 

When applying the Hamming distance to compare binary strings of different lengths, how is the dissimilarity computed?

The curse of dimensionality refers to the phenomenon where which distance measure becomes less effective as the data dimensionality increases?