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中文摘要：
通过对真实世界蚁群的模拟仿真,提出一种基于随机游走的约束蚂蚁聚类算法来处理以must-link和can-not-link形式出现的约束聚类问题.在人工数据集和UCI标准数据集上的实验结果表明我们的算法优于无监督的蚁群聚类算法和COP-Kmeans算法.
英文摘要：
By simulating the clustering behaviors of the real-world ant colonies,we propose in this paper a constrained ant clustering algorithm based on random walk to deal with the constrained clustering problems with pairwise must-link and cannot-link constraints.Experimental results show that our approach is more effective on both synthetic datasets and UCI datasets compared with the unsupervised random walk ant-based clustering algorithm and the COP-Kmeans algorithm.
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