# External Cluster Validity Measures

In this section, we review the external cluster validity scores that are implemented in the genieclust package for Python and R [20] and discussed in detail in [22] (this section contains excerpts therefrom).

Let $$\mathbf{y}$$ be a label vector representing one of the reference $$k$$-partitions $$\{X_1,\dots,X_k\}$$ of a benchmark dataset1 $$X$$, where $$y_i\in\{1,\dots,k\}$$ gives the true cluster number (ID) of the $$i$$-th object.

Furthermore, let $$\hat{\mathbf{y}}$$ be a label vector encoding another partition, $$\{\hat{X}_1,\dots,\hat{X}_k\}$$, which we would like to relate to the reference one, $$\mathbf{y}$$. In our context, we assume that $$\hat{\mathbf{y}}$$ has been determined by some clustering algorithm.

External cluster validity measures are functions of the form $$I(\mathbf{y}, \hat{\mathbf{y}})$$ such that the more similar the partitions, the higher the score. They are normalised so that identical label vectors return the highest similarity score which is equal to 1. Some measures can be adjusted for chance, yielding approximately 0 for random partitions.

Oftentimes, partition similarity scores (e.g., the adjusted Rand index or the normalised mutual information score) are used as $$I$$s. They are symmetric in the sense that $$I(\mathbf{y}, \hat{\mathbf{y}})= I(\hat{\mathbf{y}}, \mathbf{y})$$. However, as argued in [22], in our context we do not need this property because the reference label vector $$\mathbf{y}$$ is considered fixed. The adjusted asymmetric accuracy is an example of such a non-symmetric measure.

## Confusion Matrix

Let $$\mathbf{C}$$ be the confusion (matching) matrix corresponding to $$\mathbf{y}$$ and $$\hat{\mathbf{y}}$$, where $$c_{i,j}=\#\{ u: y_u=i\text{ and }\hat{y}_u=j \}$$ denotes the number of points in the true cluster $$X_i$$ and the predicted cluster $$\hat{X}_j$$ with $$i,j,\in\{1,\dots,k\}$$.

$\begin{split} \begin{array}{|c||cccc|} \hline \mathbf{c}_{1,\cdot} & c_{1,1} & c_{1,2} & \cdots & c_{1,k} \\ \mathbf{c}_{2,\cdot} & c_{2,1} & c_{2,2} & \cdots & c_{2,k} \\ \mathbf{c}_{3,\cdot} & c_{3,1} & c_{3,2} & \cdots & c_{3,k} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \mathbf{c}_{k,\cdot} & c_{k,1} & c_{k,2} & \cdots & c_{k,k} \\ \hline\hline n & \mathbf{c}_{\cdot,1} & \mathbf{c}_{\cdot,2} & \cdots & \mathbf{c}_{\cdot,k} \\ \hline \end{array} \end{split}$

It holds $$\sum_{i=1}^k \sum_{j=1}^k c_{i,j} = n$$. Moreover, let $$\mathbf{c}_{i,\cdot} = \sum_{j=1}^k c_{i,j} = \#\{ u: y_u=i \}$$ denote the number of elements in the reference cluster $$X_i$$ and $$\mathbf{c}_{\cdot,j} = \sum_{i=1}^k c_{i,j} = \#\{ u: \hat{y}_u=j \}$$ be the number of objects in the predicted cluster $$\hat{X}_j$$.

All the measures reviewed here are expressed solely by means of operations on confusion matrices. Therefore, we will be using the notation $$I(\mathbf{y}, \hat{\mathbf{y}})$$ and $$I(\mathbf{C})$$ interchangeably.

## Illustration

The scatterplots depicting the reference and a few example partitions of the wut/x2 dataset are displayed below. We also report the confusion matrices and the values of the validity measures discussed in the sequel.

Actually, both Genie and the k-means method output some quite reasonable partitions (as we mentioned in an earlier section, there might be many equally valid groupings). Still, here we only want to relate them to the current reference set.

## Why Not the Simple Accuracy?

A common mistake is to compute the standard accuracy as known from the evaluation of classification models:

$\mathrm{A}_\text{do not use it}^\mathrm{:(}(\mathbf{C}) = \sum_{i=1}^k \frac{c_{i, i}}{n},$

which is the proportion of “correctly classified” points. This measure is of no use in clustering, because clusters are defined up to a permutation of the sets’ IDs.

In other words, if the predicted cluster #1 is identical to the reference cluster #3, this should be treated as a perfect match.

## Set-Matching Measures

In our context, the predicted clusters need to be matched with the true ones somehow. To do so, we can seek a permutation $$\sigma$$ of the set $$\{1,2,\dots,k\}$$ which is a solution to (see, e.g., [9]):

$\text{maximise}\ \sum_{i=1}^k c_{i,\sigma(i)} \qquad \text{w.r.t. }\sigma.$

This guarantees that one column is paired with one and only one row in the confusion matrix.

### Pivoted and Normalised Accuracy

Optimal pairing leads to what we call here the pivoted accuracy (classification rate [49] or classification accuracy [40]):

$\mathrm{PA}(\mathbf{C}) = \max_\sigma \frac{1}{n} \sum_{i=1}^k c_{i, \sigma(i)}.$

This relies on the best matching of the labels in $$\mathbf{y}$$ to the labels in $$\mathbf{\hat{y}}$$ so as to maximise the standard accuracy.

Unfortunately, PA is not adjusted for chance. The smallest possible value it can take is $$1/k$$. We can thus consider the normalised accuracy:

$\mathrm{NA}(\mathbf{C}) = \max_\sigma \frac{ \frac{1}{n} \sum_{i=1}^k c_{i, \sigma(i)} - \frac{1}{k} }{ 1-\frac{1}{k} }.$

Implementation: genieclust.compare_partitions.normalized_accuracy.

Still, if there are clusters of highly imbalanced sizes, then its value is biased towards the quality of the match in the largest point group.

In [22], the adjusted for both cluster sizes and chance version of PA was proposed. Namely, the adjusted asymmetric accuracy is defined as:

$\mathrm{AAA}(\mathbf{C}) = \frac{ \max_\sigma \frac{1}{k} \sum_{i=1}^k \frac{c_{i, \sigma(i)}}{c_{i, \cdot}} - \frac{1}{k} }{ 1 - \frac{1}{k} }= 1- \min_\sigma \left( \frac{1}{k} \sum_{i=1}^k \frac{c_{i, \cdot}-c_{i, \sigma(i)}}{ \frac{k-1}{k} c_{i, \cdot} } \right).$

The measure is quite easily interpretable: it is the overall percentage of correctly classified points in each cluster (one minus average classification error). It also fulfils many desirable properties.

Adjusted asymmetric accuracy is the only measure reviewed here that is not symmetric, i.e., it gives some special treatment to the reference partition. As argued in [22], this is a perfectly fine behaviour in our context, where we validate the predicted partitions.

Note

The optimal relabelling (permutation $$\sigma$$) can be determined by normalising each row of the confusion matrix so that the elements therein sum to 1:

C = np.array([  # an example confusion matrix
[12, 37,  1],
[40,  0,  0],
[0,   0, 30]
])
(C_norm := C / C.sum(axis=1).reshape(-1, 1))
## array([[0.24, 0.74, 0.02],
##        [1.  , 0.  , 0.  ],
##        [0.  , 0.  , 1.  ]])

and then by calling:

(o := genieclust.compare_partitions.normalizing_permutation(C_norm) + 1)
## array([2, 1, 3])

Note that indexing in Python is 0-based, hence the +1 part. Here is a version of the confusion matrix with the columns reordered accordingly:

C[:, o-1]
## array([[37, 12,  1],
##        [ 0, 40,  0],
##        [ 0,  0, 30]])

### Pair Sets Index

If the symmetry property is required, the pair sets index [46] can be used as a partition similarity score. In the case of partitions of the same cardinalities, it reduces to:

$\mathrm{PS}(\mathbf{C}) = \max\left\{0, \max_\sigma \frac{ \frac{1}{k} \sum_{i=1}^k \frac{ c_{i, \sigma(i)} }{ \max\{ c_{i,\cdot}, c_{\cdot, \sigma(i)} \} } - E }{ 1 - E } \right\},$

where the correction-for-chance term, assuming that $$c_{(i),\cdot}$$ is the $$i$$-th largest row sum and that $$c_{\cdot, (i)}$$ is the $$i$$-th largest column sum, was derived under the assumption of the hypergeometric distribution (see [31]):

$E= \frac{1}{k} \sum_{i=1}^{k} \frac{ c_{(i),\cdot}\, c_{\cdot, (i)} }{ n\, \max\{ c_{(i),\cdot}, c_{\cdot, (i)} \} }.$

Furthermore, we can consider a simplified variant of the pair sets index, PS, also proposed in [46]. Under the assumption that the expected score is $$E = 1/k$$, we get:

$\mathrm{SPS}(\mathbf{C}) = \max\left\{0, \max_\sigma \frac{ \frac{1}{k} \sum_{i=1}^k \frac{ c_{i, \sigma(i)} }{ \max\{ c_{i,\cdot}, c_{\cdot, \sigma(i)} \} } - \frac{1}{k} }{ 1 - \frac{1}{k} } \right\}.$

Implementation: genieclust.compare_partitions.pair_sets_index.

## Counting Concordant and Discordant Point Pairs

Another class of indices is based on counting point pairs that are concordant:

• $$\mathrm{YY} = \#\left\{ i<j : y_i = y_j\text{ and }\hat{y}_i = \hat{y}_j\right\} = \sum_{i=1}^k \sum_{j=1}^k {c_{i,j} \choose 2}$$,

• $$\mathrm{NN} = \#\left\{ i<j : y_i \neq y_j\text{ and }\hat{y}_i \neq \hat{y}_j\right\} = {n \choose 2} - \mathrm{YY} - \mathrm{NY} - \mathrm{YN}$$,

and those that are concordant:

• $$\mathrm{NY} = \#\left\{ i<j : y_i \neq y_j\text{ but }\hat{y}_i \neq \hat{y}_j\right\} = \sum_{i=1}^k {c_{i,\cdot} \choose 2} - \mathrm{YY}$$,

• $$\mathrm{YN} = \#\left\{ i<j : y_i = y_j\text{ but }\hat{y}_i \neq \hat{y}_j\right\} = \sum_{i=1}^k {c_{\cdot,j} \choose 2} - \mathrm{YY}$$;

see [31] for discussion.

### Rand Score

The Rand index [45] is simply defined as the classification accuracy:

$\mathrm{R}(\mathbf{C}) = \frac{\mathrm{YY}+\mathrm{NN}}{{n \choose 2}} = \frac{ 2T - (P+Q) + {n \choose 2} }{ {n \choose 2} },$

where $$T=\sum_{i=1}^k \sum_{j=1}^k {c_{i,j} \choose 2}$$, $$P=\sum_{i=1}^k {c_{i, \cdot} \choose 2}$$, $$Q=\sum_{i=1}^k {c_{\cdot, j} \choose 2}$$, and $$c_{i,j}=\#\{ u: y_u=i\text{ and }\hat{y}_u=j \}$$.

Implementation: genieclust.compare_partitions.rand_score.

### Fowlkes–Mallows Score

The Fowlkes–Mallows index [16] is defined as the geometric mean between precision and recall:

$\mathrm{FM}(\mathbf{C}) = \frac{ \mathrm{YY} }{ \sqrt{(\mathrm{YY}+\mathrm{YN})(\mathrm{YY}+\mathrm{NY})} } = \frac{ T }{ \sqrt{P Q} }.$

Implementation: genieclust.compare_partitions.fm_score.

Unfortunately, the lowest possible values of both indices, equal to $$0$$, can only be attained for the smallest $$n$$s. What is more, for large $$n$$ and partitions generated totally at random, their expected values and lower bounds are $$1-2(k-1)/k^2$$ and $$1/k$$, respectively.

To remedy the latter problem, an adjusted-for-chance version of the Rand index was proposed in [31]:

$\mathrm{AR}(\mathbf{C}) = \frac{R-E}{M-E}= \frac{{n \choose 2}T - PQ}{ {n \choose 2} (P+Q)/2 - PQ},$

where $$R$$ is the Rand index, $$M=1$$ is the maximal possible index value, and $$E$$ is the expected Rand index when cluster memberships are assigned randomly.

In [31], the hypergeometric model for randomness was assumed, i.e., where the partitions are picked at random given the current $$n$$ and $$k$$, namely:

$\mathbb{E}\left( \sum_{i=1}^k \sum_{j=1}^k {c_{i,j} \choose 2} \right) = \frac{ \left( \sum_{i=1}^k {c_{i,\cdot} \choose 2} \right) \left( \sum_{j=1}^k {c_{\cdot,j} \choose 2} \right) }{ {n \choose 2} }.$

A similar adjustment can be applied onto the FM index. However, it then tends to be very similar to AR.

Let us also note that these scores use $$1/{n \choose 2}$$ as the unit of information, which might cause problems with their interpretability. The adjusted asymmetric accuracy and the pair sets index work on the $$1/n$$ scale.

## Information-Theoretic Measures

The normalised mutual information [35] (denoted with $$\mathrm{NMI}_\mathrm{sum}$$ in [55]) is given by:

$\mathrm{NMI}(\mathbf{C}) = \frac{ \sum_{i=1}^k \sum_{j=1}^k \frac{c_{i,j}}{n}\, \log \frac{n c_{i,j}}{c_{i,\cdot} c_{\cdot,j}} }{ 0.5\left(\sum_{i=1}^k \frac{c_{i,\cdot}}{n} \log \frac{c_{i,\cdot}}{n} + \sum_{j=1}^k \frac{c_{\cdot,j}}{n} \log \frac{c_{\cdot,j}}{n}\right) }.$

Unfortunately, particular values of the score are rather difficult to interpret.

Implementation: genieclust.compare_partitions.normalized_mi_score.

In [55], also different adjusted versions of the above are considered. However, in [46], it is noted that, amongst others, $$\mathrm{AMI}_\mathrm{sum}$$ is strongly correlated with $$\mathrm{NMI}_\mathrm{sum}$$.

1

We assume that any potential noise points in $$X$$ have been removed before the data analysis.