关键词: 重心点, 密度, 网格, 动态, 聚类, 边界点

Abstract: In order to solve the existence problem of the manual set threshold and repeated queries in the neighborhood and processing boundary point in the traditional grid clustering algorithm based on density tree, the article proposes the improvement of clustering algorithm based on density tree and grid. This algorithm uses the average density of calculation all the grid values as density threshold, which can avoid bias artificially set the density threshold；In addition, this algorithm uses the adaptive algorithm to determine the density of the radius, which can be applied to dynamic clustering; Thus, in the querying neighborhood point, using unlabeled points of neighborhood outside as next core can avoid repeated queries in the neighborhood; The last, processing boundary point is able to enhance the accuracy of clustering algorithm. The experiments show that this algorithm greatly improved time and precision efficiency, and can better adapt to dynamic clustering.

Key words: center of gravity, density, grid, dynamic, clustering, boundary point