result; (1)特征提取与选择:从原始数据集中提取并选择最具代表性的特征; (2)聚类算法设计:根据问题的特点设计聚类算法; (3)结果评估:评估聚类结果,判断算法的有效性; (4)结果说明:对聚类结果...grid-basedclusteringalgorithms and thedensity-basedclusteringalgorithms. 这种聚类算法的基本思想是将原始数据空间更改...
格状为基础的分群法(grid-based clustering):处理时间与资料多寡无关,而是取决於格的数量 ex:sting,qlique,waveclustermodel-ba… systw.net|基于2个网页 3. 基础分群法 基础分群法(Grid-based Clustering)。切割式分群法主要是将资料分为K 群的演算 ...
Zhuang, Y., Pan, J., Wu, G.: Energy-optimal grid-based clustering in wireless microsen- sor networks with data aggregation. International Journal of Parallel, Emergent and Dis- tributed Systems 25(6), 531-550 (2010)Y. Zhuang, J. Pan and G. Wu, "Energy-optimal grid-based clustering...
直接网格算法（Grid-based Clustering）是什么意思？直接网格算法（Grid-based Clustering）是什么意思？将...
In order to solve this problem we proposed an adaptive grid -based clustering method. Our focus is on providing up-to-date arbitrary shaped clusters along with improving the processing time and bounding the amount of the memory u sage. In our method (B+C tree), a structure called "B+...
直接网格算法（Grid-based Clustering）有什么优点？直接网格算法（Grid-based Clustering）有什么优点？运算...
(CLustering In QUEst) [2], STING (STatistical INformation Grid) [3], MAFIA (Merging of Adaptive Intervals Approach to Spatial Data Mining) [4], Wave Cluster [5]and O-CLUSTER (Orthogonal partitioning CLUSTERing) [6], as a survey andalso compare their effectiveness in clustering data objects....
Hierarchical clusteringAutoGrid3D-QSARThe selection of the most appropriate protein conformation is a crucial aspect in molecular docking experiments. In order to reduce the errors arising from the use of a single protein conformation, several authors suggest the use of several tridimensional structures ...
影像匹配论文阅读(1)UGC: Real-Time, Ultra-Robust Feature Correspondence via Unilateral Grid-Based Clustering ZHAOHUI ZHENGYONG MA, HONG ZHENG, JIANPING JU, AND MINGYU LIN 摘要:快速建立两个特征集之间的可靠对应关系是特征匹配的一项具有挑战性的任务。然而,特征匹配成功的关键不仅在于匹配的鲁棒性,还在于...
A Mean Approximation Approach to a Class of Grid-Based Clustering Algorithms一类数据空间网格化聚类算法的均值近似方法聚类网格基于密度的均值近似误差估计In recent years, the explosively growing amount of data in numerous clustering tasks has attracted considerable interest in boosting the existing clustering ...