空间数据库反向最近邻聚类方法

针对当前空间数据库聚类方法未考虑降维后的距离特征反向结果, 导致空间数据分量失真, 存在聚类精度低、 耗时长的问题, 提出一种空间数据库反向最近邻聚类方法. 首先, 通过选取训练样本集实现核矩阵的特征分解, 获得其距离特征修正值去除初始值的影响; 然后, 根据核主成分分析（KPCA）降维并结合降维后的距离特征反向结果, 利用反向最近邻聚类方法与扩展的部分失真搜索法相结合, 实现空间数据的聚类; 最后利用选定的聚类中心对数据集进行计算, 计算数据集第一维分量与聚类中心第一维分量之间的失真, 得到反向最近邻, 直至所有空间数据均找到所属类别, 最终完成空间数据库反向最近邻聚类. 实验结果表明, 该方法提高了空间数据的聚类精度, 减少了空间数据聚类所用时间.

Reverse Nearest Neighbor Clustering Method of Spatial Database

Aiming at the problem that the current spatial databaseclustering method did not consider the inverse result of the distance feature after dimensionality reduction, which led to the distortion of spatial data components, the clustering accuracy was low and the time consuming was long, the author proposed a reverse nearest neighbor clustering method of spatial database. Firstly,the feature decomposition of the kernel matrix was realized by selecting the training sample set, and the distance feature correction value was obtained to remove the influence of initial value.Secondly, according to dimensionality reduction of kernel principal component analysis (KPCA) combined with the inverse result of distance feature after dimensionality reduction, the inverse nearest neighbor clustering method was combined with the extended partial distortion search method to realize spatial data clustering. Finally, the data set was calculated by using the selected clustering center, the distortion between the first dimension component of the data set and the first dimension component of the clustering center was calculated, thereverse nearest neighbor was obtained until all spatial data found the category, and the reverse nearest neighbor clustering of spatial database was completed.The experimental results show that the method improves the clustering accuracyof spatial data and reduces the time used for spatial data clustering.