Abstract Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it has received renewed attention recently as it has shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which is the focus of this paper. We propose a new metaheuristic algorithm, the Dual Iterative Local Search, and prove its ability to produce quality results for the constrained clustering problem. We compare the results obtained by this proposal to those obtained by the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.

中文翻译：

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DILS：通过双重迭代局部搜索进行约束聚类

摘要 聚类一直是知识发现的有力工具。传统上是无监督的，最近重新受到关注，因为它在提供新类型的信息时表现出更好的结果，从而导致了一种新的半监督学习：约束聚类。这种技术是传统聚类的推广，它考虑了由约束编码的附加信息。约束可以以实例级 must-link 和 cannot-link 约束的形式给出，这是本文的重点。我们提出了一种新的元启发式算法，即双重迭代局部搜索，并证明了其为约束聚类问题产生高质量结果的能力。