关键词: 聚类, 密度峰值, 截断距离参数, 局部密度

Abstract: A density-based clustering algorithm, called Clustering by Fast Search and Find of Density Peaks（CFSFDP）, is fast and need few parameters, but it exists two drawbacks that cut-off distance parameter and clustering centers must be given subjectively. To overcome these shortcomings, adaptive fast search density peak clustering algorithms is proposed. For estimating the cut-off distance parameter, the local density entropy is established and then cut-off distance parameter is adaptively estimated by minimizing the entropy. For obtaining clustering centers, the product of local density and distance, which exists the jumping character form data point to clustering centers, is used to ensure threshold, then cluster centers are gotten. The experimental results show that the proposed algorithm can achieve better clustering performance without artificial determination of cut-off distance parameters and clustering centers.

Key words: clustering, density peak, cut-off distance parameter, local density