自适应聚合策略优化的密度峰值聚类算法

Optimized Density Peak Clustering Algorithm by Adaptive Aggregation Strategy

针对密度峰值聚类算法受人为干预影响较大和参数敏感的问题,即不正确的截断距离dc会导致错误的初始聚类中心,而且在某些情况下,即使设置了适当的dc值,仍然难以从决策图中人为选择初始聚类中心。为克服这些缺陷,提出一种新的基于密度峰值的聚类算法。该算法首先根据K近邻的思想来确定数据点的局部密度,然后提出一种新的自适应聚合策略,即首先通过算法给出阈值判断初始类簇中心,然后依据离初始类簇中心最近分配剩余点,最后通过类簇间密度可达来合并相似类簇。在实验中,该算法在合成和实际数据集中的表现比DPC、DBSCAN、KNNDPC和K-means算法要好,能有效提高聚类准确率和质量。

Aiming at the problem that the density peak clustering algorithm is greatly influenced by human intervention and parameter is sensitive,that is the improper selection of its parameter cutoff distance dc will lead to the wrong selection of initial cluster centers.And in some cases,even the proper value of dc is set,initial cluster centers are still difficult to be selected from the decision graph artificially.To overcome these defects,a new clustering algorithm based on density peak is proposed.Firstly,the algorithm determines the local density of data points according to the idea of K-nearest neighbors,and then a new adaptive aggregation strategy is proposed,which firstly determines the initial cluster center by the threshold of the algorithm,then allocates the remaining points according to the nearest cluster center,and finally merges the similar clusters by the density reachable between the clusters.In the experiment,the algorithm performs better than the DPC,DBSCAN,KNNDPC and K-means algorithm in the synthetic and actual datasets,and the algorithm can effectively improve clustering accuracy and quality.