An Extension to the Automatic Cross-Association Method with 3-dimensional Matrices

Abstract

There are numerous 2-dimensional matrix data for clustering including a set of documents, citation networks, web graphs, etc. However, many real-world datasets have more than three modes which require at least 3-dimensional matrices or tensors. Focusing on the clustering algorithm known as crossassociation, we extend the algorithm to deal with a 3-dimensional matrix. Our proposed method is fully automated, and simultaneously discovers clusters of both row, column, and tube groups. Experiments on real and synthetic datasets show that our method is effective. Through the proposed method, useful information can be obtained even from sparse datasets.