计算机科学 ›› 2016, Vol. 43 ›› Issue (9): 209-212.doi: 10.11896/j.issn.1002-137X.2016.09.041
庞天杰,梁吉业
PANG Tian-jie and LIANG Ji-ye
摘要： 混合数据聚类是聚类分析中一个重要的问题。现有的混合数据聚类算法主要是在全体样本的相似性度量的基础上进行聚类,因此对大规模数据进行聚类时,算法效率不高。基于此,设计了一种新的抽样策略,在此基础上,提出了一种基于抽样的大规模混合数据聚类集成算法。该算法对利用新的抽样策略得到的多个样本子集分别进行聚类,并将结果集成得到最终聚类结果。实验证明,与改进的K-prototypes算法相比,该算法的效率有了显著提高,同时聚类有效性指标基本相同。
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