A3S: A General Active Clustering Method with Pairwise Constraints
This work addresses scalability issues in active clustering for researchers and practitioners dealing with large datasets, though it appears incremental as it builds on existing cluster-adjustment schemes.
The paper tackles the problem of high query costs in active clustering for large datasets by proposing the A3S framework, which uses adaptive clustering and adjustment based on information theory to improve clustering quality with fewer human queries, achieving significant reductions in queries across real-world datasets.
Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying. Conventional approaches with semi-supervised clustering schemes encounter high query costs when applied to large datasets with numerous classes. To address these limitations, we propose a novel Adaptive Active Aggregation and Splitting (A3S) framework, falling within the cluster-adjustment scheme in active clustering. A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In particular, our cluster adjustment is inspired by the quantitative analysis of Normalized mutual information gain under the information theory framework and can provably improve the clustering quality. The proposed A3S framework significantly elevates the performance and scalability of active clustering. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries compared with existing methods.