Journal
TSINGHUA SCIENCE AND TECHNOLOGY
Volume 28, Issue 6, Pages 1050-1062Publisher
TSINGHUA UNIV PRESS
DOI: 10.26599/TST.2022.9010056
Keywords
Heuristic algorithms; Clustering algorithms; Data science; Approximation algorithms; Iterative algorithms; Artificial intelligence; Convergence; Constrained $k$-means; Must-Link (ML) and Cannot-Link (CL) constraints; approximation algorithm; constrained clustering
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Constrained clustering with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information has been extensively studied due to its broad applications. However, no algorithm has provided a non-trivial approximation ratio to the constrained k-means problem. To address this issue, we propose an algorithm with a provable approximation ratio of O(log k/) when only ML constraints are considered. Experimental results show that our algorithm outperforms existing greedy-based heuristic methods in clustering accuracy.
Constrained clustering, such as k -means with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information as the constraints, has been extensively studied recently, due to its broad applications in data science and AI. Despite some heuristic approaches, there has not been any algorithm providing a non-trivial approximation ratio to the constrained k -means problem. To address this issue, we propose an algorithm with a provable approximation ratio of O(log k/ when only ML constraints are considered. We also empirically evaluate the performance of our algorithm on real-world datasets having artificial ML and disjoint CL constraints. The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy.
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