Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Cluster analysis plays a crucial component in consumer behavior segment. The density peak clustering algorithm (DPC) is a novel density-based clustering method. However, it performs poorly in high-dimension datasets and the local density for boundary points. In addition, its fault tolerance is affected by one-step allocation strategy. To overcome these disadvantages, an adaptive density peak clustering algorithm based on dimensional-free and reverse k-nearest neighbors (ERK-DPC) is proposed in this paper. First, we compute Euler cosine distance to obtain the similarity of sample points in high-dimension datasets. Then, the adaptive local density formula is used to measure the local density of each point. Finally, the reverse k-nearest neighbor idea is added on two-step allocation strategy, which assigns the remaining points accurately and effectively. The proposed clustering algorithm is experiments on several benchmark datasets and real-world datasets. By comparing the benchmarks, the results demonstrate that the ERK-DPC algorithm superior to some state-of- the-art methods.