A strategy for interpreting and visualizing the results of matrix-trifactorization-based coclustering algorithms - Castro, Ais B. R.; Peres, Sarajane M.; Freitas Junior, Waldyr L. de; Pirozelli, Paulo; Cozman, Fábio G.; Brandão, Anarosa A. F. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e Computacional, 2023)
Information yielded by unsupervised learning is often hard to interpret due to the lack of defined labels. To overcome this, we propose and illustrate a strategy for interpreting and visualizing the results of coclustering algorithms based on trifactorization. Our method consists of three steps: (1) vector space visualization; (2) cluster characterization by top documents/words; and (3) cocluster characterization by comparing top words between different clusters. The latter allows exploring the resulting clusters in a way which considers the relationship between attribute cluster and data cluster for every data cluster, instead of just the data cluster with the highest association with this attribute cluster. We illustrate the use of our method for the Non-negative Block Value Decomposition on a dataset of scientific abstracts.