Method of particles in visual clustering of multi-dimensional and large data sets
Method of particles in visual clustering of multi-dimensional and large data sets
复制标题
DOI：
10.1016/s0167-739x(98)00081-8
复制
发表时间：
1999-04-01
期刊：
影响因子：
--
通讯作者：
Blasiak, J
中科院分区：
文献类型：
--
作者：
Dzwinel, W;Blasiak, J
提出了一种用于N维数据集可视化聚类的方法。它基于经典的特征提取技术--萨蒙映射。在Sammon准则最小化中使用的粒子方法使该方法更可靠、更通用、更有效。为了显示其可靠性，给出了测试结果，这些测试结果是为了举例说明算法对数据错误的免疫力。强调了该方法的共性，并讨论了其在多组分分析中的作用。由于基于粒子方法的方法具有内在的并行性，视觉聚类技术可以很容易地在并行环境中实现。结果表明，映射算法的并行实现能够实现由10(4)个以上多维数据点组成的数据集的可视化。该方法在HP/Convex SPP/1600上的PVM、MPI和数据并行环境下进行了测试。本文对这三种接口的并行算法性能进行了比较。本文提出的可视化聚类方法可用于大型多维数据集的可视化和分析。(C)1999 Elsevier Science B.V.保留所有权利。
A method dedicated for visual clustering of N-dimensional data sets is presented. It is based on the classical feature extraction technique - the Sammon's mapping. This technique empowered by a particle approach used in the Sammon's criterion minimization makes the method more reliable, general and efficient. To show its reliability, the results of tests are presented, which were made to exemplify the algorithm 'immunity' from data errors. The general character of the method is emphasized and its role in multicriterial analysis discussed. Due to inherent parallelism of the methods, which are based on the particle approach, the visual clustering technique can be implemented easily in parallel environment. It is shown that parallel realization of the mapping algorithm enables the visualization of data sets consisting of more than 10(4) multi-dimensional data points. The method was tested in the PVM, MPI and data parallel environments on an HP/Convex SPP/1600. In this paper, the authors compare the parallel algorithm performance for these three interfaces. The approach to visual clustering, presented in the paper, can be used in visualization and analysis of large multi-dimensional data sets. (C) 1999 Elsevier Science B.V. All rights reserved.