Explainable Deep Clustering for Unsupervised Pattern Discovery in Complex Datasets

Abstract

When there is no linear structure in the data, it is hard to discover patterns without supervision; if the data is high dimensional, discovery becomes more challenging; if there are heterogeneous variables in the data, the pattern discovery is more difficult; if there are ambiguous boundaries between groups, the discovery is more difficult. Deep clustering is a combination of representation learning and clustering to learn latent clusters without any predefined clusters, explainability is to make the learned patterns explainable and auditable. An integrative review of explainable deep clustering for complex data is presented, and the connection between representation learning, cluster assignment, dimensionality reduction, cluster validation, and explainability post hoc is highlighted. The review reveals a main gap – the patterns extracted with deep clustering techniques are frequently evaluated based on internal indices or external labels, while the semantic validity, stability, and human usefulness of the extracted patterns is less systematically addressed. The paper suggests a modular approach to use data auditing, representation learning, constrained clustering, structural exploration using UMAP, silhouette-based validation, feature-level interpretation, and stability analysis. Results are qualitative analytical results obtained from literature, not experimental results measured. The analysis shows that deep clustering can uncover a group structure that is nonlinear and cannot be captured by conventional distance-based clustering, and that embeddings and visualizations are not evidence of natural categories, in and of themselves. The proposed workflow will focus on reporting uncertainty, careful interpretation of cluster labels, domain validation, and reproducibility. Fuzzy clustering has been a well-known method for pattern discovery since it was first proposed.Fuzzy clustering is a well-known technique for pattern discovery since proposed that was popular before the advent of deep clustering techniques.