# Similarity and Distance Measures in proxyC

#### 2022-10-06

This vignette explains how proxyC compute the similarity and distance measures.

## Notation

$\vec{x} = [x_i, x_{i + 1}, \dots, x_n] \\ \vec{y} = [y_i, y_{i + 1}, \dots, y_n]$ The length of the vector $$n = ||\vec{x}||$$, while $$|\vec{x}|$$ is the absolute values of the elements.

Operations on vectors are element-wise:

$\vec{z} = \vec{x}\vec{y} \\ n = ||\vec{x}|| = ||\vec{y}|| =||\vec{z}||$

Summation of the elements of vectors is written using sigma without specifying the range:

$\sum{\vec{x}} = \sum_{i=1}^{n}{x_i}$

When the elements of the vector is compared with a value in a pair of square brackets, the summation is counting the number of elements that equal (or unequal) to the value:

$\sum{[\vec{x} = 1]} = \sum_{i=1}^{n}{[x_i = 1]}$

## Similarity Measures

Similarity measures are available in proxyC::simil().

### Cosine similarity (“cosine”)

$simil = \frac{\sum{\vec{x}\vec{y}}}{\sqrt{\sum{\vec{x} ^ 2}} \sqrt{\sum{\vec{y} ^ 2}}}$

### Pearson correlation coefficient (“correlation”)

$simil = \frac{Cov(\vec{x},\vec{y})}{Var(\vec{x}) Var(\vec{y})}$

### Jaccard similarity (“jaccard” and “ejaccard”)

The values of $$x$$ and $$y$$ are Boolean for “jaccard”.

$e = \sum{\vec{x} \vec{y}} \\ w = \text{user-provided weight} \\ simil = \frac{e}{\sum{\vec{x} ^ w} + \sum{\vec{y} ^ w} - e}$

### Dice similarity (“dice” and “edice”)

The values of $$x$$ and $$y$$ are Boolean for “dice”.

$e = \sum{\vec{x} \vec{y}} \\ w = \text{user-provided weight} \\ simil = \frac{2 e}{\sum{\vec{x} ^ w} + \sum{\vec{y} ^ w}}$

### Hamann similarity (“hamann”)

$e = \sum{\vec{x} \vec{y}} \\ n = ||\vec{x}|| = ||\vec{y}|| \\ u = n - e \\ simil = \frac{e - u}{e + u}$

### Faith similarity (“faith”)

$t = \sum{[\vec{x} = 1][\vec{y} = 1]} \\ f = \sum{[\vec{x} = 0][\vec{y} = 0]} \\ n = ||\vec{x}|| = ||\vec{y}|| \\ simil = \frac{t + 0.5 f}{n}$

### Simple matching (“matching”)

$simil = \sum{[\vec{x} = \vec{y}]}$

## Distance Measures

Similarity measures are available in proxyC::dist(). Smoothing of the vectors can be performed when method is “chisquared”, “kullback”, “jefferys” or “jensen”: the value of smooth will be added to each element of $$\vec{x}$$ and $$\vec{y}$$.

### Manhattan distance (“manhattan”)

$dist = \sum{|\vec{x} - \vec{y}|}$

### Canberra distance (“canberra”)

$dist = \frac{|\vec{x} - \vec{y}|}{|\vec{x}| + |\vec{y}|}$

### Euclidian (“euclidian”)

$dist = \sum{\sqrt{\vec{x}^2 + \vec{y}^2}}$

### Minkowski distance (“minkowski”)

$p = \text{user-provided parameter} \\ dist = \Bigl( \sum{|\vec{x} - \vec{y}| ^ p} \Bigr) ^ \frac{1}{p}$

### Hamming distance (“hamming”)

$dist = \sum{[\vec{x} \ne \vec{y}]}$

### The largest difference between values (“maximum”)

$dist = \max{\vec{x} - \vec{y}}$

### Chi-squared divergence (“chisquared”)

$O_{ij} = \text{augmented matrix from } \vec{x} \text{ and } \vec{y} \\ E_{ij} = \text{matrix of expected count for } O_{ij} \\ dist = \sum{\frac{(O_{ij} - E_{ij}) ^ 2}{ E_{ij}}} \\$

### Kullback–Leibler divergence (“kullback”)

$\vec{p} = \frac{\vec{x}}{\sum{\vec{x}}} \\ \vec{q} = \frac{\vec{y}}{\sum{\vec{y}}} \\ dist = \sum{\vec{q} \log_2{\frac{\vec{q}}{\vec{p}}}}$

### Jeffreys divergence (“jeffreys”)

$\vec{p} = \frac{\vec{x}}{\sum{\vec{x}}} \\ \vec{q} = \frac{\vec{y}}{\sum{\vec{y}}} \\ dist = \sum{\vec{q} \log_2{\frac{\vec{q}}{\vec{p}}}} + \sum{\vec{p} \log_2{\frac{\vec{p}}{\vec{q}}}}$

### Jensen-Shannon divergence (“jensen”)

$\vec{p} = \frac{\vec{x}}{\sum{\vec{x}}} \\ \vec{q} = \frac{\vec{y}}{\sum{\vec{y}}} \\ \vec{m} = \frac{1}{2} (\vec{p} + \vec{q}) \\ dist = \frac{1}{2} \sum{\vec{q} \log_2{\frac{\vec{q}}{\vec{m}}}} + \frac{1}{2} \sum{\vec{p} \log_2{\frac{\vec{p}}{\vec{m}}}}$

## References

• Choi, S.-S., Cha, S.-H., & Tappert, C. C. (2010). A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics, 8(1), 43–48.
• Nielsen, F. (2019). On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means. Entropy, 21(5), 485. https://doi.org/10.3390/e21050485
• Jain, G., Mahara, T., & Tripathi, K. N. (2020). A Survey of Similarity Measures for Collaborative Filtering-Based Recommender System. In M. Pant, T. K. Sharma, O. P. Verma, R. Singla, & A. Sikander (Eds.), Soft Computing: Theories and Applications (pp. 343–352). Springer. https://doi.org/10.1007/978-981-15-0751-9_32