Data Science Distance: When Close Seems Far

Distance measures lie at the core of many data science methods. Euclidean distance can measure the distance between two points in space, Levenshtein distance can measure the difference between two words, and cosine distance can measure the angular distance between two vectors. Finding the right sense of distance is of integral importance in data science. Else, a method can produce "optimal" results under a distance measure but not match our physical and cognitive experience. In this article, we will see visually how the choice of distance in colors fundamentally impacts an algorithm. In particular, different measures result in different mosaics, including mosaics produced by colorful candies and emoji.