北京科技大学学报Issue(12)：1703-1711,9.DOI:10.13374/j.issn1001-053x.2014.12.018
基于共享最近邻密度的演化数据流聚类算法
Evolving data stream clustering algorithm based on the shared nearest neighbor density
摘要
Abstract
Existing density-based data stream clustering algorithms are difficult to discover clusters with different densities and to distinguish clusters with bridges and the outliers. A novel stream clustering algorithm was proposed based on the shared nearest neigh-bor density. In this algorithm, the shared nearest neighbor density was defined based on the shared nearest neighbor graph, which con-sidered the degree of data object surrounded by the nearest neighbors and the degree of data object demanded by around data objects. So the clustering result was not influenced by the density variation. The average distance of data object and the cluster density were de-fined to identify outliers and clusters with bridges. The updating algorithm over the sliding window was designed to maintain the renewal of clusters on the shared nearest neighbor graph. Theoretical analysis and experimental results demonstrate the performance of clustering effect and a better clustering quality.关键词
数据流/聚类算法/最近邻/离群点/数据挖掘Key words
data streams/clustering algorithms/nearest neighbors/outliers/data mining分类
信息技术与安全科学引用本文复制引用
高兵,张健沛,邹启杰..基于共享最近邻密度的演化数据流聚类算法[J].北京科技大学学报,2014,(12):1703-1711,9.基金项目
国家自然科学基金资助项目(61370083,61073043,61073041,61402126) （61370083,61073043,61073041,61402126）
