Robust Qualitative Data Clustering via Learnable Multi-Metric Space Fusion

Understanding categorical data with vague qualitative values by forming clusters is crucial in many data-driven AI fields. Compared with numerical data with its quantitative values embedded in well-defined Euclidean distance space, distances of the qualitative values are naturally unknown and are specially defined for certain data types or tasks. This paper, therefore, proposes a distance metric space fusion framework, which learns to fuse multiple distance metrics to form a statistical information-complete and prior knowledge-comprehensive metric for robust and accurate cluster analysis of qualitative data. To better serve various clustering tasks, the metric fusion objective is incorporated into the clustering objective through iterative learning. It turns out that the proposed method stably demonstrates superiority on various challenging real benchmark datasets. Extensive experiments including significance tests, ablation studies, etc. validate its efficacy. Source code of the proposed method is available at https://github.com/Sen-Feng/ICASSP-MSF/tree/main/CODE.