Аннотация:In this paper we propose a novel method to cluster categorical data while retaining their context.Typically, clustering is performed on numerical data.However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts.Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t-SNE to develop a a context-aware clustering approach (using pre-trained word embeddings).We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
