利用流形学习进行空间信息服务分类
Research on Geo\|spatial Web Services Classification Based on Manifold Learning
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摘要: 对现有的Web服务分类方法中存在的问题进行了分析,阐述了流形和流形学习的概念以及将流形学习引入到Web服务领域的目的,提出了利用流形学习进行空间信息服务分类的方法。该方法在空间信息服务降维前后保持各服务间的相似(近邻)关系不变,并通过对服务进行降维可视化指导,确定初始聚类数和聚类中心,从而提高利用聚类分析实现空间信息服务无监督分类的精度。实验表明,本文方法不仅能够对抽象的Web服务进行数值化表示,而且能够有效地提高服务分类的性能。Abstract: The problems existed in the traditional methods of Web services classification are analyzed, the concepts of manifold and manifold learning and the purpose of introducing the manifold learning into the Web services are described. The algorithm for the visualization and classification of geo\|spatial Web services(GWS) based on manifold learning is proposed.During the process of dimension reduction, the similarity between GWS is preserved and the data manifold is unrolled. In order to improve the precision of classification, we gain the mapping rule from the GWS to the 2D data and the initial number of clusters according to the visualization of 2D mapping data. The experimental results prove the validity of the improved visualization and classification algorithm for GWS proposed in this paper.