[1]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]
Copy

An improved clustering algorithm that searches and finds density peaks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 2 Page number: 229-235 Column: 学术论文—机器学习 Public date: 2017-05-05
Title:
An improved clustering algorithm that searches and finds density peaks
Author(s):
GAN Wenyan;  LIU Chong
College of Command Information System, PLA University of Science and Technology, Nanjing 210007, China
Keywords:
data mining; clustering algorithms; kernel density estimation; entropy
CLC:
TP311
DOI:
10.11992/tis.201512036
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.
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.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems