Kendall tau distance

The Kendall tau ranking distance between two lists ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}}$ is

${\displaystyle K(\tau _{1},\tau _{2})=|\{(i,j):i<j,(\tau _{1}(i)<\tau _{1}(j)\wedge \tau _{2}(i)>\tau _{2}(j))\vee (\tau _{1}(i)>\tau _{1}(j)\wedge \tau _{2}(i)<\tau _{2}(j))\}|.}$

where

• ${\displaystyle \tau _{1}(i)}$ and ${\displaystyle \tau _{2}(i)}$ are the rankings of the element ${\displaystyle i}$ in ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}}$ respectively.

${\displaystyle K(\tau _{1},\tau _{2})}$ will be equal to 0 if the two lists are identical and ${\displaystyle n(n-1)/2}$ (where ${\displaystyle n}$ is the list size) if one list is the reverse of the other. Often Kendall tau distance is normalized by dividing by ${\displaystyle n(n-1)/2}$ so a value of 1 indicates maximum disagreement. The normalized Kendall tau distance therefore lies in the interval [0,1].

Kendall tau distance may also be defined as

${\displaystyle K(\tau _{1},\tau _{2})={\begin{matrix}\sum _{\{i,j\}\in P}{\bar {K}}_{i,j}(\tau _{1},\tau _{2})\end{matrix}}}$

where

• P is the set of unordered pairs of distinct elements in ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}}$
• ${\displaystyle {\bar {K}}_{i,j}(\tau _{1},\tau _{2})}$ = 0 if i and j are in the same order in ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}}$
• ${\displaystyle {\bar {K}}_{i,j}(\tau _{1},\tau _{2})}$ = 1 if i and j are in the opposite order in ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}.}$

Kendall tau distance can also be defined as the total number of discordant pairs.

Kendall tau distance in Rankings: A permutation (or ranking) is an array of N integers where each of the integers between 0 and N-1 appears exactly once. The Kendall tau distance between two rankings is the number of pairs that are in different order in the two rankings. For example, the Kendall tau distance between 0 3 1 6 2 5 4 and 1 0 3 6 4 2 5 is four because the pairs 0-1, 3-1, 2-4, 5-4 are in different order in the two rankings, but all other pairs are in the same order.[1]

If Kendall tau function is performed as ${\displaystyle K(L1,L2)}$ instead of ${\displaystyle K(\tau _{1},\tau _{2})}$ (where ${\displaystyle \tau _{1}}$ and ${\displaystyle \tau _{2}}$ are the rankings of ${\displaystyle L1}$ and ${\displaystyle L2}$ elements respectively), then triangular inequality is not guaranteed. The triangular inequality fails in cases where there are repetitions in the lists. So then we are not dealing with a metric anymore.

Suppose one ranks a group of five people by height and by weight:

Here person A is tallest and third-heaviest, and so on.

In order to calculate the Kendall tau distance, pair each person with every other person and count the number of times the values in list 1 are in the opposite order of the values in list 2.

Since there are four pairs whose values are in opposite order, the Kendall tau distance is 4. The normalized Kendall tau distance is

${\displaystyle {\frac {4}{5(5-1)/2}}=0.4.}$

A value of 0.4 indicates that 40% of pairs differ in ordering between the two lists.

Given two rankings ${\displaystyle \tau _{1},\tau _{2}}$, it is possible to rename the items such that ${\displaystyle \tau _{1}=(1,2,3,...)}$. Then, the problem of computing the Kendall tau distance reduces to computing the number of inversions in ${\displaystyle \tau _{2}}$ --- the number of index pairs ${\displaystyle i,j}$ such that ${\displaystyle i<j}$ while ${\displaystyle \tau _{2}(i)>\tau _{2}(j)}$. There are several algorithms for calculating this number.

• A simple algorithm based on merge sort requires time ${\displaystyle O(n\log n)}$.[2]
• A more advanced algorithm requires time ${\displaystyle O(n{\sqrt {\log {n}}})}$.[3]

• Kendall tau rank correlation coefficient
• Spearman's rank correlation coefficient
• Kemeny–Young maximum-likelihood voting rule

• Fagin, R.; Kumar, R.; Sivakumar, D. (2003). "Comparing top k lists". SIAM Journal on Discrete Mathematics. 17 (1): 134–160. CiteSeerX 10.1.1.86.3234. doi:10.1137/S0895480102412856.
• Kendall, M. (1948). "Rank Correlation Methods". Charles Griffin & Company Limited. Cite journal requires |journal= (help)
• Kendall, M. (1938). "A New Measure of Rank Correlation". Biometrika. 30 (1/2): 81–89. doi:10.2307/2332226. JSTOR 2332226.

• Online software: computes Kendall's tau rank correlation