Optimized Density Peak Clustering Algorithm by Adaptive Aggregation Strategy

Aiming at the problem that the density peak clustering algorithm is greatly influenced by human interven-tion 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.