摘要： 针对传统K中心聚类算法存在的初始化敏感、聚类结果多样化等问题，提出一种基于密度的K中心聚类方案，并与序列比对、动态规划等方法有机地融合在一起，实现了对核酸序列的聚类分析。实验表明，该方案与传统K中心聚类算法相比较，初始化较理想，迭代次数较少，聚类效果更优。

关键词: K中心聚类, 直接密度可达, 序列比对, 动态规划, 生物信息学

Abstract: Due to the disadvantages of initialization and result in the K-medoids clustering algorithm, a new density-based K-medoids clustering is described. And it combines sequence alignment, dynamic programming and other theories, accomplishes the clustering analysis in the nucleic acid sequences. Experiments prove that this method has better initialization, less iterative times and satisfying results compared with the ordinary K-medoids clustering.

Key words: K-medoids cluster, Direct arrived density, Sequence alignment, Dynamic programming, Bioinformatics