Abstract
Mutual information clustering is an agglomerative hierarchical clustering method that has been used to group random variables or sets thereof. Some researchers have found that the normalization method used can lead to oddly-sized clusters that do not line up with expected results. We introduce a new normalization parameter to control the size of the clusters, and apply it to food allergy data from a large allergy repository from an electronic health record, treating the distributions of food allergies in our population as random variables. Our method was able to identify previously known food cross-reaction groups (with an adjusted Rand index of 0.971, outperforming alternative clustering algorithms), in addition to proposing possible new groups. Our results demonstrate the viability of mutual information clustering as an approach for discovering possible food cross-reactions.