聚类效用:生物序列聚类的新度量

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI:10.1109/CSBW.2005.38

Jason Lee, Sun Kim

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引用次数: 2

摘要

我们提出了聚类效用(CU)，这是一种基于考虑聚类内部相似性和聚类之间差异的度量，没有度量空间假设。CU与质量指标有很高的相关性。CU可以很好地扩展数据大小，并且无论数据大小如何变化，其与质量指数的强相关性几乎是不变的。CU可以用于两种方式:指导序列聚类算法和评估聚类结果。

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Cluster utility: a new metric for clustering biological sequences

We propose cluster utility (CU), a metric that is based on consideration of similarity within a cluster and difference between clusters without metric space assumption. CU showed a very high correlation with the quality index. CU scales very well with data size and its strong correlation with quality index was nearly invariable regardless of data size change. CU can be used in two ways: to guide sequence clustering algorithms and to evaluate clustering results.

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2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)

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