分群演算法是現今找出相同類別（高相似性）資料相當普遍的知識發現工具，雜訊（noise）是演算法判定該筆資料不隸屬於任何一群之結果，有時雜訊的產生是因該筆資料本為無用資訊，然而亦有因收集資料之環境、遮蔽物、傳感器（sensor）老舊或演算法參數設定不正確等因素之可能性，此時這些有意義之雜訊將失去其背後資訊意義而被誤判且遭大多數移除雜訊之方法省略而導致分群準確度下降。
本篇論文中，我們針對以密度為基礎之分群方法所得到之雜訊進行修正，利用DBSCAB演算法原有的兩參數定義修正方法之限制，這些有意義的雜訊將被修正至正確的群或自成一群。我們將此精練DBSCAN雜訊的演算法稱為RaC-DBSCAN。在最後的實驗章節中我們採納混合資料集、UCI資料集和真實現實中之資料集並和原始方法比較，我們的演算法有效的提高分群的準確度並充分的使用被誤分為雜訊之資料。

Clustering algorithms are efficient for the task of class identification in spatial databases. Noise after clustering sometimes is meaningful due to mistake by inappropriate parameters setting or environmental factor in collecting data, we call them “dirty data”. Removal of these noise methods loss considerable information because ignoring dirty data which in some part is meaningful.
In this paper, we present a method to refine the result of density-based clustering which two parameters assist our proposed definition complete. We performed kinds of experimental evaluation of effectiveness of refinement cooperating with DBSCAN, most famous density-based clustering algorithm in various application, called RaC-DBSCAN in synthetic dataset, UCI dataset and real dataset. The results of our experiments demonstrate that RaC-DBSCAN no matter enhance precision of identify each cluster but also generate potential by further utilize dirty data.

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