首页> 外文会议>International Conference on Statistics in Science, Business, and Engineering >Influential data subsets labelling: A fuzzy relation approach
【24h】

Influential data subsets labelling: A fuzzy relation approach

机译:有影响的数据子集标签:模糊关系方法

获取原文

摘要

The selection of candidate for influential data subsets is an important step in regression analysis in order to construct a model of high quality. One of the most popular and widely used methods for labelling influential data subsets is 1-clustering method. This is an indexed hierarchical clustering based on the notion of subdominant ultrametric (SDU) of a dissimilarity matrix. In the literature, there are many different algorithms available to construct SDU. However, the computational complexity of those algorithms is high; the running time is very slow especially when the sample size is large. In this paper a method based on fuzzy relation approach, which allows us to construct a promising algorithm to obtain SDU even for a large number of data, is introduced. An example will be presented and discussed to illustrate the advantage of the proposed algorithm.
机译:有影响性数据子集的候选者的选择是回归分析的重要步骤,以构建高质量的模型。用于标记有影响性数据子集的最流行和广泛使用的方法之一是1个聚类方法。这是基于异构性矩阵的子域超空心(SDU)的概念的索引分层聚类。在文献中,有许多不同的算法可用于构建SDU。但是,这些算法的计算复杂性很高;当样本大小很大时,运行时间非常慢。在本文中,一种基于模糊关系方法的方法,它允许我们构建有前途的算法来获得SDU,即使对于大量数据而言。将呈现和讨论一个示例以说明所提出的算法的优点。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号