一种改进的搜索密度峰值的聚类算法OA北大核心CSCDCSTPCD
An improved clustering algorithm that searches and finds density peaks
聚类是大数据分析与数据挖掘的基础问题.刊登在2014年《Science》杂志上的文章《Clustering by fast search and find of density peaks》提出一种快速搜索密度峰值的聚类算法,算法简单实用,但聚类结果依赖于参数dc的经验选择.论文提出一种改进的搜索密度峰值的聚类算法,引入密度估计熵自适应优化算法参数.对比实验结果表明,改进方法不仅可以较好地解决原算法的参数人为确定的不足,而且具有相对更好的聚类性能.
Clustering is a fundamental issue for big data analysis and data mining.In July 2014, a paper in the Journal of Science proposed a simple yet effective clustering algorithm based on the idea that cluster centers are characterized by a higher density than their neighbors and having a relatively large distance from points with higher densities.The proposed algorithm can detect clusters of arbitrary shapes and differing densities but is very sensitive to …查看全部>>
淦文燕;刘冲
解放军理工大学 指挥信息系统学院,江苏 南京 210007解放军理工大学 指挥信息系统学院,江苏 南京 210007
信息技术与安全科学
数据挖掘聚类算法核密度估计熵
data miningclustering algorithmskernel density estimationentropy
《智能系统学报》 2017 (2)
基于拓扑势的复杂网络结构演化研究
229-236,8
国家自然科学基金项目(60974086).
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