ACDM: An Effective and Scalable Active Clustering with Pairwise Constraint

Clustering is fundamentally a subjective task: a single dataset can be validly clustered in various ways, and without further information, clustering systems cannot determine the appropriate clustering to perform. This underscores the importance of integrating constraints into clustering, enabling users to convey their preferences to the system. Active constraint-based clustering approaches prioritize the identification of the most valuable constraints to inquire about, striving to achieve effective clustering with the minimal number of constraints needed. We propose an <u>A</u> ctive <u>C</u> lustering with <u>D</u> iffusion <u>M</u> odel (ACDM). ACDM applies the nearest-neighbor technique to construct a diffusion graph, and utilizes an online framework to refine the clustering result iteratively. In each iteration, (a) nodes with high uncertainty and representativeness are selected in batch mode, (b) then a novel neighborhood-set-based query is used for categorizing the selected nodes, using pairwise constraints, and (c) the categorized nodes are used as source nodes in the diffusion model for cluster refinement. We experimentally demonstrate that ACDM outperforms state-of-the-art methods in terms of clustering quality and scalability.