A Fast Density Clustering Algorithm Based on Reference

YAN An; LIU Qi-lin

Chinese

您当前的位置：

首页 >

文章列表页 >

A Fast Density Clustering Algorithm Based on Reference

更新时间：2026-09-04

- A Fast Density Clustering Algorithm Based on Reference
- Vol. 34, Issue 10, Pages: 32-35(2017)
- 作者机构：
  
  合肥工业大学管理学院,安徽,合肥,230000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
闫安, 刘琪林. 一种基于参考点的快速密度聚类算法[J]. 微电子学与计算机, 2017, 34(10): 32-35, 41.

YAN An, LIU Qi-lin. A Fast Density Clustering Algorithm Based on Reference[J]. 2017, 34(10): 32-35.
闫安, 刘琪林. 一种基于参考点的快速密度聚类算法[J]. 微电子学与计算机, 2017, 34(10): 32-35, 41. DOI：

YAN An, LIU Qi-lin. A Fast Density Clustering Algorithm Based on Reference[J]. 2017, 34(10): 32-35. DOI：

摘要

提出了一种基于个参考点DCUR (Density-based Clustering Using k References). 新算法以个参考点反应了数据的分布状态

然后基于参考点对数据进行聚类分析.该算法保持了DBSCAN的优点

并且可以减少区域查询次数

降低I/O开销.理论和实验证明新算法能够有效地对大规模数据库进行聚类

且其执行效率明显高于传统的基于*树的DBSCAN算法.

Abstract

In this paper

a new kind of clustering algorithm that is called DCUR (density-based clustering using k references) is presented. Innovative point is that the new algorithm to k references response data Distribution

and then analyzes the data based on the k references. DCUR keeps the ability of density based clustering methods good features

and it can reach high efficiency and the execution frequency of region query can be decreased

and consequently the I/O cost is reduced

so the new algorithm can reach high efficiency and reduce the complexity of the algorithm. Both theory analysis and experimental results confirm that DCUR is effective and efficient in clustering large-scale database

and its executing efficiency is much higher than traditional DBSCAN algorithm based on R*-tree.

关键词

Keywords

references

Ng R, Han J. Efficient and effective clustering methods for spatial data mining[C]//Proc. of the 20th Conference on VLDB. Santiago, Chile, 1994: 144-155.

New York Kaufan L, Rousseeuw P J. Finding groups in data: an Introduction to cluster analysis[M]. New York: John Wiley and Son, NY, 1990: 145-193. DOI: 145

Ester M, Kriegel H P, 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: AAAI Press, 1996: 226-231.

Guhas Rastogir, Shim K. Cure: an efficient data clustering method for very large databases[C]//Proceedings of the ACM SIGMOD international conference on Management of data. New York, ACM Press, 1996: 103-114.

许虎, 王治和. 一种改进的基于密度的聚类算法[J]. 微电子学与计算机, 2012, 29(2): 44-53. DOI: 44

曾依灵, 许洪波, 白硕. 改进的OPTICS算法以及其在文本聚类中的应用[J]. 中文信息学报, 2008, 22(1): 51-55. DOI: 51

管涛. 统计聚类模型研究综述[J]. 计算机科学, 2012, 39(7): 18-24. DOI: 18

郝晓丽, 张靖. 基于改进自适应聚类算法的RBF神经网络分类器设计与实现[J]. 计算机科学, 2014, 41(6): 260-263. DOI: 260

周开乐, 杨善林, 丁帅, 等. 聚类有效性研究综述[J]. 系统工程理论与实践, 2014, 34(9): 2417-2431. DOI: 2417

赵杰, 杨柳. 聚类分析算法dBscan的改进与实现[J]. 微电子学与计算机, 2009, 26(11): 190-192. DOI: 190

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the design of blockchain-based AI-generated content traceability framework

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Steganography model guided by attention mechanism and improved dense convolution

Implementation and verification of a dual-chain power gating strategy for low power design

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰