An Efficient Density-based Clustering Algorithm Combined with Representative Set

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Abstract Cluster analysis is a key technique of data mining and has been applied widely. The paper presents a novel clustering algorithm $DCURS$, which incorporates the principle of proximity and limited area into the density-based clustering method to improve clustering accuracy and stability. In addition, $DCURS$ introduces the representative set to accelerate the running speed of the algorithm. Experimental results verify that $DCURS$ has better performance than traditional density-based algorithms of $DBSCAN$ and $DBRS$.
DOI: 10.12733/jics20101716

Key words： Data Mining Density-based Clustering the Principle of Proximity Limited Area Representative Set

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	Articles by authors
	Hongfang Zhou
	Xiao Wang
	Xuehan Zhao

Cite this article:

Hongfang Zhou,Xiao Wang,Xuehan Zhao. An Efficient Density-based Clustering Algorithm Combined with Representative Set[J]. , 2013, 10(7): 2021-2028.

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http://manu35.magtech.com.cn/Jwk_ics/EN/ OR http://manu35.magtech.com.cn/Jwk_ics/EN/Y2013/V10/I7/2021

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