Efficient algorithm for the k-means problem with Must-Link and Cannot-Link constraints

Journal article

Jia, Chaoqi, Guo, Longkun, Liao, Kewen and Lu, Zhigang. (2023). Efficient algorithm for the k-means problem with Must-Link and Cannot-Link constraints. Tsinghua Science and Technology. 28(6), pp. 1050-1062. https://doi.org/10.26599/TST.2022.9010056
AuthorsJia, Chaoqi, Guo, Longkun, Liao, Kewen and Lu, Zhigang
Abstract

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(logk) 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.

Keywordsconstrained k-means; Must-Link (ML) constraints; Cannot-Link (CL) constraints; approximation algorithm; constrained clustering
Year2023
JournalTsinghua Science and Technology
Journal citation28 (6), pp. 1050-1062
PublisherTsinghua University
ISSN1007-0214
Digital Object Identifier (DOI)https://doi.org/10.26599/TST.2022.9010056
Scopus EID2-s2.0-85166738954
Open accessPublished as ‘gold’ (paid) open access
Page range1050-1062
FunderNational Natural Science Foundation of China (NSFC)
Universities of Shandong Province
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online28 Jul 2023
Publication process dates
Deposited10 Oct 2023
Grant ID12271098
61772005
2020KJN008
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