关键词: 聚类分析, 网格, 密度, 残差分析

Abstract:

In order to solve the problem of clustering by fast search and ?nd of density peaks that relies on truncation distance, computational complexity and need of artificial selection initial seeds, a self-confirming clustering algorithm based on residual error and density grid is proposed. The data object is mapped to a grid, with a mesh object as clustering objects, and remove the grid object without any information. Then compute the density and distance values of grid objects in a specific way. Then the grid objects with cluster centers are determined by residual analysis. The distance with the non-edge points and the self-changing threshold are used to process the edge points and noise points of the grid. The simulation experiment shows that the proposed algorithm has higher clustering accuracy and faster execution speed compared with some other clustering algorithms.

Key words: cluster analysis, grid, density, residual analysis