As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, based on density peak limited effect varying distribution (VDD), equilibrium (ED), multiple domain-density maximums (MDDM), leading to the problems sparse cluster loss fragmentation. To address these problems, we propose ...