计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 41-45.doi: 10.11896/j.issn.1002-137X.2015.06.009
冯晨菲,杨燕,王红军,徐英歌,王韬
FENG Chen-fei, YANG Yan, WANG Hong-jun, XU Ying-ge and WANG Tao
摘要: 现有的半监督聚类集成方法能利用先验信息,使集成的准确性、鲁棒性和稳定性得到提高,但在集成阶段加入成对约束信息时,只考虑了给定的约束信息而忽视了约束点与被约束点的邻域点之间的关系。针对此问题,提出了一种基于数据相关性的半监督模糊聚类集成方法。该方法首先利用半监督模糊聚类算法建立集成信息矩阵,并将其转换为相似性矩阵;然后,利用已知的约束信息及约束点与被约束点的邻域点之间的关系来修改相似性矩阵;最后,利用图划分算法得到最终的聚类结果。真实数据上的实验结果表明,提出的方法可以有效提高聚类质量。
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