[1]周治平,王杰锋,朱书伟,等.一种改进的自适应快速AF-DBSCAN聚类算法[J].智能系统学报编辑部,2016,11(1):93-98.[doi:10.11992/tis.201410021]
 ZHOU Zhiping,WANG Jiefeng,ZHU Shuwei,et al.An improved adaptive and fast AF-DBSCAN clustering algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(1):93-98.[doi:10.11992/tis.201410021]
点击复制

一种改进的自适应快速AF-DBSCAN聚类算法(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年1期
页码:
93-98
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
An improved adaptive and fast AF-DBSCAN clustering algorithm
作者:
周治平 王杰锋 朱书伟 孙子文
江南大学物联网工程学院, 江苏无锡 214122
Author(s):
ZHOU Zhiping WANG Jiefeng ZHU Shuwei SUN Ziwen
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
关键词:
密度聚类DBSCAN区域查询全局参数KNN分布数学统计分析
Keywords:
density clusteringDBSCANregion queryglobal parametersKNN distributionmathematical statistics and analysis
分类号:
TP181
DOI:
10.11992/tis.201410021
摘要:
基于密度的DBSCAN聚类算法可以识别任意形状簇,但存在全局参数Eps与MinPts的选择需人工干预,采用的区域查询方式过程复杂且易丢失对象等问题,提出了一种改进的参数自适应以及区域快速查询的密度聚类算法。根据KNN分布与数学统计分析自适应计算出最优全局参数Eps与MinPts,避免聚类过程中的人工干预,实现了聚类过程的全自动化。通过改进种子代表对象选取方式进行区域查询,无需漏检操作,有效提高了聚类的效率。对4种典型数据集的密度聚类实验结果表明,本文算法使得聚类精度提高了8.825%,聚类的平均时间减少了0.92 s。
Abstract:
The density-based DBSCAN clustering algorithm can identify clusters with arbitrary shape, however, the choice of the global parameters Eps and MinPts requires manual intervention, the process of regional query is complex and loses objects easily. Therefore, an improved density clustering algorithm with adaptive parameter for fast regional queries is proposed. Using KNN distribution and mathematical statistical analysis, the optimal global parameters Eps and MinPts are adaptively calculated, so as to avoid manual intervention and enable full automation of the clustering process. The regional query is conducted by improving the selection manner of the object, which is represented by a seed and thus avoiding manual intervention, and so the clustering efficiency is effectively increased. The experiment results looking at density clustering of four typical data sets show that the proposed method effectively improves clustering accuracy by 8.825% and reduces the average time of clustering by 0.92 s.

参考文献/References:

[1] 吉根林, 姚瑶. 一种分布式隐私保护的密度聚类算法[J]. 智能系统学报, 2009, 4(2):137-141. JI Genlin, YAO Yao. Density-based privacy preserving distributed clustering algorithm[J]. CAAI transactions on intelligent systems, 2009, 4(2):137-141.
[2] SMITI A, ELOUEDI Z. DBSCAN-GM:An improved clustering method based on Gaussian means and DBSCAN techniques[C]//2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES). Lisbon, 2012:573-578.
[3] ZHANG Jiashu, KEREKES J. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data[J]. Geoscience and remote sensing letters, 2015, 12(4):726-730.
[4] MIMAROGLU S, AKSEHIRLI E. Improving DBSCAN’s execution time by using a pruning technique on bit vectors[J]. Pattern Recognition Letters, 2011, 32(13):1572-1580.
[5] JIANG Hua, LI Jing, YI Shenghe, et al. A new hybrid method based on partitioning-based DBSCAN and ant clustering[J]. Expert systems with applications, 2011, 38(8):9373-9381.
[6] BORAH B, BHATTACHARYYA D K. An improved sampling-based DBSCAN for large spatial databases[C]//Proceedings of International Conference on Intelligent Sensing and Information Processing(ICISIP). Chennai, India, 2004:92-96.
[7] KELLNER D, KLAPPSTEIN J, DIETMAYER K. Grid-based DBSCAN for clustering extended objects in radar data[C]//2012 IEEE Intelligent Vehicles Symposium. Alcal de Henares, Madrid, Spain, 2012:365-370.
[8] ZHOU Hongfang, Wang Peng, LI Hongyan. Research on adaptive parameters determination in DBSCAN algorithm[J]. Journal of information & computational science, 2012, 9(7):1967-1973.
[9] YUE Shihong, LI Ping, GUO Jidong, et al. A statistical information-based clustering approach in distance space[J]. Journal of Zhejiang university science, 2005, 6A(1):71-78.
[10] MA Yu, GAO Yuling, SONG Shaoyun. The algorithm of DBSCAN based on probability distribution[C]//5th International Symposium on IT in Medicine and Education. Xining, China, 2014:2785-2792.
[11] JAHIRABADKAR S, KULKARNI P. Algorithm to determine ε-distance parameter in density based clustering[J]. Expert systems with applications, 2014, 41(6):2939-2946.
[12] XIONG Zhongyang, CHEN Ruotian, ZHANG Yufang, et al. Multi-density DBSCAN algorithm based on density levels partitioning[J]. Journal of information and computational science, 2012, 9(10):2739-2749.
[13] LIU Bing. A fast density-based clustering algorithm for large databases[C]//2006 International Conference on Machine Learning and Cybernetics. Dalian, China, 2006:996-1000.
[14] 夏鲁宁.SA-DBSCAN:一种自适应基于密度聚类算法[J]. 中国科学院研究生院学报, 2009, 26(4):530-538. XIA Luning. SA-DBSCAN:A self-adaptive density-based clustering algorithm[J]. Journal of the graduate school of the Chinese academy of sciences, 2009, 26(4):530-538.
[15] 周水庚, 周傲英, 曹晶, 等. 一种基于密度的快速聚类算法[J]. 计算机研究与发展, 2000, 37(11):1287-1292. ZHOU Shuigeng, ZHOU Aoying, CAO Jing, et al. A fast density-based clustering algorithm[J]. Journal of computer research & development, 2000, 37(11):1287-1292.

相似文献/References:

[1]陆剑锋,郭茂祖,张昱,等.基于时空约束密度聚类的停留点识别方法[J].智能系统学报编辑部,2020,15(1):59.[doi:10.11992/tis.201910026]
 LU Jianfeng,GUO Maozu,ZHANG Yu,et al.Stay point recognition method based on spatio-temporal constraint density clustering[J].CAAI Transactions on Intelligent Systems,2020,15(1):59.[doi:10.11992/tis.201910026]

备注/Memo

备注/Memo:
收稿日期:2014-10-13;改回日期:。
基金项目:国家自然科学基金资助项目(61373126);江苏省产学研联合创新资金-前瞻性联合研究基金资助项目(BY2013015-33).
作者简介:周治平,男,1962年生,教授,博士,主要研究方向为检测技术与自动化装置、信息安全等;王杰锋,男,1989年生,硕士研究生,主要研究方向为智能信息处理;朱书伟,男,1990年生,硕士研究生,主要研究方向为数据挖掘与人工智能。
通讯作者:王杰锋.E-mail:18352513420@163.com.
更新日期/Last Update: 1900-01-01