Clustering with Complex Constraints — Algorithms and Applications

Clustering with constraints is an important and developing area. However, most work is confined to conjunctions of simple together and apart constraints which limit their usability. In this paper, we propose a new formulation of constrained clustering that is able to incorporate not only existing types of constraints but also more complex logical combinations beyond conjunctions. We first show how any statement in conjunctive normal form (CNF) can be represented as a linear inequality. Since existing clustering formulations such as spectral clustering cannot easily incorporate these linear inequalities, we propose a quadratic programming (QP) clustering formulation to accommodate them. This new formulation allows us to have much more complex guidance in clustering. We demonstrate the effectiveness of our approach in two applications on text and personal information management. We also compare our algorithm against existing constrained spectral clustering algorithm to show its efficiency in computational time.

Authors

• Weifeng Zhi University of California, Davis
• Xiang Wang
• Buyue Qian
• Patrick Butler Virginia Tech
• Naren Ramakrishnan Virginia Tech
• Ian Davidson

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