Label Propagation Clustering Algorithm Based on Adaptive Angle

The direction-based label propagation clustering (DBC) algorithm needs to set the number of neighbors (k) and the angle value (degree), which are highly sensitive. Moreover, DBC algorithm is not suitable for datasets with uneven neighbor density distribution. To overcome above problems, we propose an improved DBC algorithm based on adaptive angle and label redistribution (ALR-DBC). The ALR-DBC algorithm no longer input parameter degree, but dynamically adjusts the deviation angle through the concept of high-low density region to determine the receiving range. This flexible receiving range is no longer affected by the uneven distribution of neighbor density. Finally, those points that do not meet the expectations of the main direction are redistributed. Experiments show that the ALR-DBC algorithm performs better than DBC algorithm in most artificial datasets and real datasets. It is also superior to the classical algorithms listed. It also has good experimental results when applied to wireless sensor data annotation.