Evolutionary Clustering with DBSCAN

被引：0

作者：

Zhang, Yuchao ^{[1
]}

Liu, Hongfu ^{[2
]}

Deng, Bo ^{[1
]}

机构：

[1] Beijing Inst Syst Engn, Beijing, Peoples R China

[2] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China

来源：

2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2013年

关键词：

Terms; Evolutionary Clustering; Density; Based; Clustering; DBSCAN;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering algorithms have been used in the field of data mining for such a long time. With the accumulation of the online data sets, studies on cluster evolution were carried out so as to decrease noise and maintain continuity of clustering results. A number of evolutionary clustering algorithms have been proposed, such as the evolutionary K-means and Spectral clustering, but none of them were engaged to solving the density-based clustering problem.

引用

页码：923 / 928

页数：6

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