An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

doi:10.1007/s11633-016-1038-7

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	An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference
	S. Nagaraju; Manish Kashyap; Mahua Bhattachraya
刊名	International Journal of Automation and Computing
	2017
卷号	14 期号:1 页码:57-67
关键词	Density based clustering neighborhood difference density-based spatial clustering of applications with noise (DBSCAN) space density indexing (SDI) core object.
ISSN号	1476-8186
DOI	10.1007/s11633-016-1038-7
文献子类	IJAC-IA-2016-01-027.pdf
英文摘要	The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise (DBSCAN) is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Nrad and minimum number of points in neighborhood Npts. So the effeciency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper: 1) We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2) We validated our experimental results using one of our authors recently proposed space density indexing (SDI) internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42466]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	Visual Information Processing Lab, Indian Institute of Information Technology and Management, Gwalior, India
推荐引用方式 GB/T 7714	S. Nagaraju,Manish Kashyap,Mahua Bhattachraya. An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference[J]. International Journal of Automation and Computing,2017,14(1):57-67.
APA	S. Nagaraju,Manish Kashyap,&Mahua Bhattachraya.(2017).An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference.International Journal of Automation and Computing,14(1),57-67.
MLA	S. Nagaraju,et al."An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference".International Journal of Automation and Computing 14.1(2017):57-67.