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矩阵配置文件 V:将领域知识纳入主题发现的通用技术

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery
课程网址: http://videolectures.net/kdd2017_dau_matrix_profile/  
主讲教师: Hoang Anh Dau
开课单位: 加州大学
开课时间: 2017-10-09
课程语种: 英语
中文简介:
时间序列主题发现可能已成为时间序列数据挖掘中最常用的原语,并且已应用于机器人、医学和气候学等多种领域。最近在主题发现的可扩展性方面取得了重大进展。然而,我们认为当前主题发现的定义是有限的,并且可能会导致用户的意图/期望与主题发现搜索结果之间不匹配。在这项工作中,我们解释了这些问题背后的原因,并引入了一个新颖且通用的框架来解决这些问题。我们的想法可以与当前最先进的算法一起使用,几乎没有时间或空间开销,并且速度足够快,可以在海量数据集上进行实时交互和假设测试。
课程简介: Time series motif discovery has emerged as perhaps the most used primitive for time series data mining, and has seen applications to domains as diverse as robotics, medicine and climatology. There has been recent significant progress on the scalability of motif discovery. However, we believe that the current definitions of motif discovery are limited, and can create a mismatch between the user's intent/expectations, and the motif discovery search outcomes. In this work, we explain the reasons behind these issues, and introduce a novel and general framework to address them. Our ideas can be used with current state-of-the-art algorithms with virtually no time or space overhead, and are fast enough to allow real-time interaction and hypotheses testing on massive datasets. We demonstrate the utility of our ideas on domains as diverse as seismology and epileptic seizure monitoring.
关 键 词: 数据集; 数据挖掘; 计算机科学
课程来源: 视频讲座网
数据采集: 2023-12-25:wujk
最后编审: 2023-12-25:wujk
阅读次数: 6