GreedyDBSCAN一种针对多密度聚类的DBSCAN改进.PDFVIP

优先出版 计 算 机 应 用 研 究 第 33 卷 Greedy DBSCAN:一种针对多密度聚类的 DBSCAN 改进算法 * 冯振华，钱雪忠，赵娜娜 (江南大学 物联网工程学院，江苏 无锡 214000) 摘 要：针对基于密度的 DBSCAN 算法对于输入参数敏感、无法聚类多密度数据集等问题，提出了一种贪心的 DBSCAN 改进算法（Greedy DBSCAN）。算法仅需输入一个参数 MinPts，采用贪心策略自适应地寻找 Eps 半径参数进行簇发现， 利用相对稠密度识别和判定噪声数据，在随机寻找核对象过程中使用邻域查询方式提升算法效率，最终通过簇的合并产 生最终的聚类结果。实验结果表明，改进后的算法能有效地分离噪声数据，识别多密度簇，聚类准确度较高。 关键词：多密度；贪心策略；相对稠密度；邻域查询；噪声数据；DBSCAN 聚类 中图分类号：TP301.6 Greedy DBSCAN: improved DBSCAN algorithm on multi-density clustering Feng Zhenhua, Qian Xuezhong, Zhao Nana (School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214000, China) Abstract: Since the DBSCAN algorithm can not handle multi-density datasets clustering problems and sensitive to input parameters. this paper proposed an improved DBSCAN algorithm based on greedy strategy. namely Greedy DBSCAN. Especially. the proposed algorithm only needs to input one parameter MinPts. and it uses greedy strategy to find the parameter Eps adaptively. Moreover. the proposed algorithm use the relative density to identify and determine the noise data. The proposed approach uses the neighborhood query in the process of random seeking core objects to improve the efficiency. And through the combination of the clusters to generate the final clustering results. The experiment results show that Greedy DBSCAN algorit