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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Competitive Learning With Pairwise Constraints
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Competitive Learning With Pairwise Constraints

机译：成对约束下的竞争性学习

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摘要

Constrained clustering has been an active research topic since the last decade. Most studies focus on batch-mode algorithms. This brief introduces two algorithms for on-line constrained learning, named on-line linear constrained vector quantization error (O-LCVQE) and constrained rival penalized competitive learning (C-RPCL). The former is a variant of the LCVQE algorithm for on-line settings, whereas the latter is an adaptation of the (on-line) RPCL algorithm to deal with constrained clustering. The accuracy results—in terms of the normalized mutual information (NMI)—from experiments with nine datasets show that the partitions induced by O-LCVQE are competitive with those found by the (batch-mode) LCVQE. Compared with this formidable baseline algorithm, it is surprising that C-RPCL can provide better partitions (in terms of the NMI) for most of the datasets. Also, experiments on a large dataset show that on-line algorithms for constrained clustering can significantly reduce the computational time.
机译：自最近十年以来，约束聚类一直是活跃的研究主题。大多数研究集中在批处理模式算法上。本简介介绍了两种用于在线约束学习的算法，分别称为在线线性约束矢量量化误差（O-LCVQE）和约束竞争者惩罚性竞争学习（C-RPCL）。前者是用于在线设置的LCVQE算法的一种变体，而后者是（在线）RPCL算法的一种改编，用于处理约束聚类。来自九个数据集的实验的准确性结果（以归一化互信息（NMI）表示）表明，由O-LCVQE诱导的分区与（批处理模式）LCVQE所发现的分区具有竞争力。与这种强大的基线算法相比，令人惊讶的是C-RPCL可以为大多数数据集提供更好的分区（就NMI而言）。同样，在大型数据集上的实验表明，用于约束聚类的在线算法可以显着减少计算时间。

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