# Mojena’s test

## Mojena’s test :

Mojena’s test is a statistical method used to evaluate the performance of a clustering algorithm. The test is named after its creator, Italian statistician Antonio Mojena, who proposed it in a paper published in 1952.
The idea behind Mojena’s test is to measure the degree to which a clustering algorithm is able to correctly group a set of data points into clusters. To do this, the test compares the clusters produced by the algorithm with a known, “true” set of clusters. The true clusters are typically determined by some external criteria, such as the labels assigned to the data points by a human annotator.
Here are two examples of how Mojena’s test can be used to evaluate the performance of a clustering algorithm:
Example 1: Suppose we have a dataset of images of animals, and we want to use a clustering algorithm to automatically group the images into clusters based on the type of animal they contain (e.g., dogs, cats, birds, etc.). To evaluate the performance of the algorithm, we can use Mojena’s test to compare the clusters produced by the algorithm with the true clusters determined by the labels assigned to the images by a human annotator.
Example 2: Suppose we have a dataset of customer transactions from a retail store, and we want to use a clustering algorithm to automatically group the transactions into clusters based on the type of product purchased (e.g., clothing, electronics, home goods, etc.). To evaluate the performance of the algorithm, we can use Mojena’s test to compare the clusters produced by the algorithm with the true clusters determined by the product categories assigned to the transactions by the store’s inventory system.
To perform Mojena’s test, we first need to determine the true clusters for the dataset. This typically involves applying some external criteria to the data points, such as the labels assigned to the data by a human annotator or the categories assigned to the data by some other external system.
Once we have determined the true clusters for the dataset, we can use them to evaluate the performance of the clustering algorithm. To do this, we compute a similarity measure between the true clusters and the clusters produced by the algorithm. The most commonly used similarity measure for this purpose is the Rand index, which measures the proportion of pairs of data points that are either in the same cluster in both the true and the predicted clusters, or in different clusters in both the true and the predicted clusters.
The Rand index can be computed using the following formula:
RI = (a + d) / (a + b + c + d)
where:
a = the number of pairs of points that are in the same cluster in both the true and the predicted clusters
b = the number of pairs of points that are in different clusters in the true clusters but in the same cluster in the predicted clusters
c = the number of pairs of points that are in the same cluster in the true clusters but in different clusters in the predicted clusters
d = the number of pairs of points that are in different clusters in both the true and the predicted clusters
The Rand index ranges from 0 to 1, with a value of 1 indicating perfect agreement between the true and predicted clusters and a value of 0 indicating no agreement.
In general, a high Rand index value indicates that the clustering algorithm is performing well, while a low Rand index value indicates that the algorithm is not performing well. However, it is important to keep in mind that the Rand index is only one of many possible measures of cluster similarity, and other measures may be more appropriate for certain types of data and clustering algorithms.