 "n"-grams can also be used for efficient approximate matching.By converting a sequence of items to a set of "n"-grams, it can be embedded in a vector space, thus allowing the sequence to be compared to other sequences in an efficient manner.For example, if we convert strings with only letters in the English alphabet into single character 3-grams, we get a formula_5-dimensional space (the first dimension measures the number of occurrences of "aaa", the second "aab", and so forth for all possible combinations of three letters).