Greedy DBSCAN:一种针对多密度聚类的DBSCAN改进算法

Greedy DBSCAN: an improved DBSCAN algorithm on multi-density clustering

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Min Pts,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。

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 needed to input one parameter MinPts, and it used greedy strategy to find the parameter Eps adaptively.Moreover,the proposed algorithm used the relative density to identify and determine the noise data. The proposed approach used 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 algorithm can separate the noise data effectively and obtain higher accuracy of identifying multi-density clusters