智能系统学报2017，Vol.12Issue(2)：229-236,8.DOI:10.11992/tis.201512036
一种改进的搜索密度峰值的聚类算法
An improved clustering algorithm that searches and finds density peaks
摘要
Abstract
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 tunable parameter dc.In this paper, we propose an improved clustering algorithm that adaptively optimizes parameter dc.The time complexity of our algorithm was super-linear with respect to the size of the dataset.Further, our theoretical analysis and experimental results show the effectiveness and efficiency of our improved algorithm.关键词
数据挖掘/聚类算法/核密度估计/熵Key words
data mining/clustering algorithms/kernel density estimation/entropy分类
信息技术与安全科学引用本文复制引用
淦文燕,刘冲..一种改进的搜索密度峰值的聚类算法[J].智能系统学报,2017,12(2):229-236,8.基金项目
国家自然科学基金项目(60974086). （60974086）
