相关实验视频

Updated: Sep 19, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

基于滑动窗口的罕见部分周期性模式挖掘算法,在时间数据流上进行挖掘

K Jyothi Upadhya¹, Ronan Lobo¹, Mini Shail Chhabra¹

¹Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.

Frontiers in big data

|June 19, 2025

概括

这项研究引入了两种新方法,R3PStreamSW-Growth和R3PStreamSW-BitVectorMiner,用于在时间数据流中找到罕见的部分周期性模式. 在各种数据集的速度和效率上,R3PStreamSW-BitVectorMiner显著超过R3PStreamSW-Growth.

科学领域:

数据挖掘数据挖掘
模式识别模式识别
大数据分析大数据分析

背景情况:

定期模式挖掘对于了解各个行业的大数据集至关重要.
现有的方法很难从时间数据流中提取罕见的部分周期性模式.
时间和流数据集需要专门的算法来分析模式的发生.

研究的目的:

提出新的算法,从时间数据流中提取罕见的部分周期性模式.
为了解决当前处理数据流中的时间信息的方法的局限性.
为实时模式挖矿开发高效的单扫描方法.

主要方法:

推出了两个基于滑动窗口的单扫描算法:R3PStreamSW-Growth和R3PStreamSW-BitVectorMiner.
专注于在时间数据流中挖掘罕见的部分周期性模式.
在密集和稀疏数据集上评估算法性能,包括事故和T10I4D100K.

主要成果:

在R3PStreamSW-BitVectorMiner上,R3PStreamSW-Growth表现出了比R3PStreamSW更高的性能.
在密集的事故数据集上观察到大约93%的性能增长.
在R3PStreamSW-BitVectorMiner的稀疏T10I4D100K数据集上发现了90%的性能提升.

结论:

R3PStreamSW-BitVectorMiner比R3PStreamSW-Growth快得多,而且效率也更高.
提出的方法有效地从时间数据流中提取罕见的部分周期性模式.
这些发现突显了R3PStreamSW-BitVectorMiner在数据流分析中的真实应用中的潜力.

关键词:

基于列表的采矿流采矿.部分周期性开采采矿.罕见的部分周期性模式采矿.罕见的周期性模式采矿采矿流是周期性模式的采矿.基于树木的溪流采矿采矿.

更多相关视频

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

相关概念视频

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

Rapidly Varying Flow

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...