【摘要】 在数据骤增的大数据时代,聚类算法成为研究热点。首先,介绍传统聚类算法与流数据聚类算法,流数据聚类算法可以达到数据快速扫描并分类形成簇集合的效果。此外,介绍基于划分的传统算法K-means、K-means++、K-中心点以及基于流数据的Stream算法;基于层次的传统算法BIRCH以及基于流数据的CluStream算法;基于密度的传统算法DNSCAN以及基于流数据的DenStream算法;基于网格的传统算法CLIQUE以及基于流数据的D-Stream算法。更多还原

【Abstract】 In the era of big data with a sudden increase in data, clustering algorithms have become a hot research topic. First, the traditional clustering algorithm and streaming data clustering algorithm are introduced. The streaming data clustering algorithm can achieve the effect of rapid dKa-t am secdaonindisn ga lagonrdi tchlmas saifnidc aStiTonR EtoA fMor mal gcolruistthemr sbetass.e Idn oand dsittrieoan,m iinntgr oddautca;e ttrraaddiittiioonnaall lpaayrteirt-i obna-sbeads eadlg aolrigtohrimth BmIs RKC-Hm aelagnosr,i tKh-m maenadn sd+a+ta,stream-based CluStream algorithm; traditional density-based algorithm DNSCAN and DenStream algorithm based on data stream;CLIQUE, a traditional grid-based algorithm, and D-Stream algorithm based on streaming data.更多还原