Abstract
Contrast sets are used in many knowledge-based systems to capture data patterns relevant to a target variable. While they have many advantages such as being highly interpretable, they do not come with a similarity measure or feature vectors for downstream tasks such as regression or classification. To address these disadvantages, we propose Con2Vec (Contrast set toVector), a method to embed contrast sets into a low-dimensional continuous vector space. Con2Vec defines two novel similarity and co-occurrence contexts for a contrast set, and then leverages a neural embedding model to learn low-dimensional continuous vectors (aka embeddings) for contrast sets. We further apply contrast set embeddings to construct the feature vectors for transactional data. We extensively evaluate our method Con2Vec on four real-world datasets, compared against state-of-the-art embedding and non-embedding methods where the results demonstrate the clear advantages of our method.