Math Data Science

How to measure the similarity of sequence values

Levenshtein distance and Kendall tau distance

Bartosz Mikulski 10 Jul 2019 – 1 min read

To measure the similarity of data sequences, we can use the methods designed to measure the similarity of strings. In this blog post, I am going to show which metrics can be used to measure the difference between ordered sequences of values, usually between two texts.

Levenshtein distance

In the case of text, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions).

That method can also be reused for measuring the distance between any pair of sequences, for example, pages visited by a user during a single session or products purchased during a user lifetime.

Kendall tau distance

Kendall tau distance is a metric of difference between rankings. It is defined as the number of swaps to be done while bubble-sorting one sequence to get the same order as the second sequence.

Similarly, we can use the metric to get the difference between any sequences. Conveniently, this metric is implemented in Scipy as the scipy.stats.kendalltau function.

Older post

How to measure the similarity of sequence values

Levenshtein distance

Kendall tau distance

Measuring document similarity in machine learning

Using machine learning for software testing

How to measure the similarity of sequence values

Levenshtein distance

Kendall tau distance

Measuring document similarity in machine learning

Using machine learning for software testing

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