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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
发表期刊International Journal of Automation and Computing
ISSN1476-8186
2017
卷号14期号:1页码:57-67
文章类型IJAC-IA-2016-01-027.pdf
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.
关键词Density based clustering neighborhood difference density-based spatial clustering of applications with noise (DBSCAN) space density indexing (SDI) core object.
DOI10.1007/s11633-016-1038-7
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被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/42466
专题学术期刊_Machine Intelligence Research
作者单位Visual Information Processing Lab, Indian Institute of Information Technology and Management, Gwalior, India
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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.
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