聚类技术的比较与分析

B. Singla, K. Yadav, J. Singh
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引用次数: 4

摘要

聚类是旁观者的看法。研究人员提出了许多原理和模型,而这些原理和模型所对应的优化问题只能通过大量的算法来近似求解。每个算法都强调它与以前算法的不同之处。在我们的实验中,我们在效率、簇内相似性和稳定性方面比较了分层聚类和分区聚类,聚类的一个理想特性是稳定性,即数据的微小变化不会导致显著不同的聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison and analysis of clustering techniques
Clustering is in the eye of beholder. Researchers have proposed many principles and models whose corresponding optimization problem can only be approximately solved by even large number of algorithms. Each algorithm emphasizes how it is different from previous algorithms. In our experiment we are comparing hierarchical and partitional clustering in terms of efficiency, intra-cluster similarity, and stability, a desirable property of clustering is stability that is small change to data should not lead dramatically different clustering.
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