• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Engineering MIS >Extending the Gaussian membership function for finding similarity between temporal patterns
【24h】

Extending the Gaussian membership function for finding similarity between temporal patterns

机译：扩展高斯隶属函数以找到时间模式之间的相似性

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, the basic Gaussian membership function is extended to design the dissimilarity measure. We extend the dissimilarity measure proposed in the G-spamine by applying normal distribution. The dissimilarity measure proposed in this paper is designed by using the concept of standard normal distribution. For a pattern to be similar, the dissimilarity between reference and the temporal pattern has to be less than or equal to the dissimilarity constraint. This dissimilarity constraint is obtained by transforming the user threshold value to z-space. The dissimilarity measure has also been extended to compute the distance bounds by devising necessary expressions. These distance bounds can be used to prune the invalid temporal associations. The algorithm to obtain the similar temporal associations is outlined.
机译：本文扩展了基本的高斯隶属度函数，以设计相异性度量。我们通过应用正态分布扩展了G-spamine中提出的相异性度量。本文采用标准正态分布的概念设计了相异度量。为了使模式相似，参考和时间模式之间的相异性必须小于或等于相异性约束。通过将用户阈值转换为z空间来获得这种相异性约束。通过设计必要的表达式，相异性度量也已扩展为计算距离范围。这些距离范围可用于修剪无效的时间关联。概述了获得相似时间关联的算法。

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