QQ客服 官方微博 反馈留言

	A Distance and Density-based Clustering Algorithm using Automatic Peak Detection
	Rong Zhou; Shuang Zhang; Chun Chen; Li Ning; Yong Zhang; Shengzhong Feng; Yi Liu; Nurbol Luktarhan
	2016
会议名称	The IEEE International Conference on Smart Cloud (SmartCloud 2016)
会议地点	美国，纽约
英文摘要	Distance-based and density-based clustering algo- rithms are often used on large spatial and arbitrary shape of data sets. However, some well-known clustering algorithms have troubles when distribution of objects in the dataset varies, and this may lead to a bad clustering result. Such bad performances are more dramatically significant on high-dimensional dataset. Recently, Rodriguez and Laio proposed an efficient clustering algorithm [1] based on two essential indicators: density and distance, which are used to find the cluster centers and play an important role in the process of clustering. However, this algorithm does not work well on high dimensional data sets, since the threshold of cluster centers has been defined ambiguously and hence it has to be decided visually and manually. In this paper, an alternative definition of the indicators is introduced and the threshold of cluster centers is automatically decided by using an improved Canopy algorithm. With fixed centers (each represents a cluster), each remaining data object is assigned to a cluster dependently in a single step. The performance of the algorithm is analyzed on several benchmarks. The experimental results show that (1) the clustering performance on some high dimensional data sets, e.g., intrusion detection, is better; and (2) on low dimensional data sets, the performances are as good as the traditional clustering algorithms.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10290]
专题	深圳先进技术研究院_数字所
作者单位	2016
推荐引用方式 GB/T 7714	Rong Zhou,Shuang Zhang,Chun Chen,et al. A Distance and Density-based Clustering Algorithm using Automatic Peak Detection[C]. 见:The IEEE International Conference on Smart Cloud (SmartCloud 2016). 美国，纽约.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。