Abstract:

Aiming at the defects of traditional density-based uncertain clustering algorithm， such as parameter sensitivity and poor clustering results for complex manifold uncertain data sets， a new uncertain data density peak clustering algorithm based on JS divergence （UDPC-JS） was proposed. The algorithm first uses the uncertain natural neighborhood density factor defined by uncertain natural neighbors to remove noise points； secondly， the local density of uncertain data objects is calculated by combining uncertain natural neighbors and JS divergence. Then， the initial clustering center of uncertain data sets is found by combining the idea of representative points， and the distance based on JS divergence and graph is defined between the initial clustering centers. Then， the local density calculated based on uncertain natural neighbors and JS divergence and the newly defined distance based on JS divergence and graph between the initial clustering centers are used to construct the decision graph on the initial clustering center， and the final clustering center is selected according to the decision graph. Finally， the unassigned uncertain data objects are assigned to the cluster where their initial clustering centers are located. The experimental results show that the algorithm has better clustering effect and accuracy than the comparison algorithm and has greater advantages in dealing with uncertain data sets of complex manifolds.

Key words: uncertain data, uncertain natural neighbors, JS divergence, density peak, clustering