Hierarchical co-clustering with augmented matrices from external domains

Abstract

Co-clustering simultaneously classifies row and column objects of data matrices and is considered to have better accuracy than conventional one-way clustering methods. In the era of big data, extracting classification knowledge about objects from several domains has become increasingly feasible. This study proposes a hierarchical co-clustering with augmented matrices (HICCAM), which co-clusters the row and column objects of a target matrix while utilizing the augmented data matrices of these target objects extracted from the external domains. The algorithm is designed to improve classification accuracy by transferring knowledge in augmented matrices and simultaneously improves cluster interpretability using hierarchical cluster structures. Experiments on document clustering confirmed that HICCAM achieved the highest accuracy among comparison methods with and without external knowledge. Its clusters exhibit hierarchical relationships according to their topics. In addition, we provide the experimental results with multiview synthetic datasets that demonstrate a clustering situation in which HICCAM can be effectively identified.