Efficient Algorithm for the k-Means Problem with Must-Link and Cannot-Link Constraints

Chaoqi Jia

doi:10.26599/TST.2022.9010056

Efficient Algorithm for the k-Means Problem with Must-Link and Cannot-Link Constraints

2023-12-01

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DOI：10.26599/TST.2022.9010056

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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.

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

Efficient Algorithm for the k-Means Problem with Must-Link and Cannot-Link Constraints

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