期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2017
卷号：8
期号：4
页码：9-13
语种：English
出版社：Ayushmaan Technologies
摘要：Now days, due to the explosive growth of huge amount of data have been uploaded into several websites. Thus it needs to be classified. Data mining is the process of extracting useful information from huge databases. Many approaches of data mining have been proposed to discover useful and accurate information among vast amount of data such as clustering, association rule mining, time series analysis and sequential pattern discovery etc. Thus, the density-based clustering algorithms have been used to find clusters based on the density of points in dense regions. Data clustering can be used in many application areas such as marketing, planning, insurance, biology, network security, earthquake, crime detections, intrusion detection systems etc. This paper presents a comparative study of various density based clustering algorithms for data miningalongwith their merits and demerits.
关键词：Clustering Algorithms;Data Mining;Density based Algorithms; DBSCAN.
