[1]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):229-235.[doi:10.11992/tis.201512036]
GAN Wenyan,LIU Chong.An improved clustering algorithm that searches and finds density peaks[J].CAAI Transactions on Intelligent Systems,2017,12(2):229-235.[doi:10.11992/tis.201512036]
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GAN Wenyan,LIU Chong.An improved clustering algorithm that searches and finds density peaks[J].CAAI Transactions on Intelligent Systems,2017,12(2):229-235.[doi:10.11992/tis.201512036]
一种改进的搜索密度峰值的聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第2期
页码:
229-235
栏目:
学术论文—机器学习
出版日期:
2017-05-05
- Title:
- An improved clustering algorithm that searches and finds density peaks
- Keywords:
- data mining; clustering algorithms; kernel density estimation; entropy
- 分类号:
- TP311
- 摘要:
- 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。
- 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.
- 参考文献/References:
-
[1] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
[2] MANYIKA J, CHUI M, BROWN B, et al. Big data: the next frontier for innovation, competition, and productivity[M]. McKinsey Global Institute, 2011.
[3] HAN Jiawei, KAMBER M, PEI Jian. Data mining: concepts and techniques[M]. 3rd ed. Burlington: Morgan Kaufmann, 2011.
[4] JAIN A K. Data clustering: 50 years beyond k-means[Z]. Pattern Recognition Letters, 2009.
[5] 唐杰, 东昱晓, 蒋朦, 等. SIGKDD二十周年庆典[J]. 中国计算机学会通讯, 2014, 10(10): 58-64.
[6] http://comments.sicencemag.org/content/10.1126/science.1242072 (请核对网址及补充文献内容)
[7] 淦文燕, 李德毅. 基于核密度估计的层次聚类算法[J]. 系统仿真学报, 2004, 16(2): 302-305. GAN Wenyan, LI Deyi. Hierarchical clustering based on kernel density estimation[J]. Journal of System Simulation, 2004, 16(2): 302-305.
[8] ESTER M, KRIEGEL H, SANDER J, et al. A density based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd international conference on knowledge discovery and data mining. Portland, 1996: 226-231.
[9] GIONIS A, MANNILA H, TSAPARAS P. Clustering aggregation[J]. ACM transactions on knowledge discovery from data, 2007, 1(1): Article No.4.
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备注/Memo
收稿日期:2015-12-31;改回日期:。
基金项目:国家自然科学基金项目（60974086）．
作者简介:淦文燕,女,副教授。主要研究方向为人工智能,数据挖掘,机器学习;刘冲,男,硕士研究生,主要研究方向为大数据分析,数据挖掘。
通讯作者:刘冲. E-mail:lc1368542460@126.com.
更新日期/Last Update:
1900-01-01