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序列数据挖掘
相关语句
  series data mining
     Research on the Model and Algorithm of Series Data Mining
     序列数据挖掘的模型和算法研究
短句来源
     Time series data mining using discrete wavelet transform
     基于离散小波变换的时间序列数据挖掘
短句来源
     Time Series Data Mining Strategy Based on Rough Set
     一种基于Rough集的时间序列数据挖掘策略
短句来源
     Time series data mining algorithm based on generalized computing
     基于广义计算的时间序列数据挖掘算法
短句来源
     Research on Time Series Data Mining and the Application to Traffic Flow Forecasting
     时间序列数据挖掘的研究以及在交通流预测上的应用
短句来源
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  sequential data mining
     Chinese Web page feature selection method based on sequential data mining
     基于序列数据挖掘的中文网页特征选择方法
短句来源
     A method is proposed to select feature candidates from Chinese websites on the basis of sequential data mining,and it is used in the model of Chinese websites classification.
     提出了一种基于序列数据挖掘的中文网页候选特征的选择方法,并用于中文网页分类模型.
短句来源
  serials data mining
     A system of time serials data mining based on combined model
     一种基于组合模型的时间序列数据挖掘系统
短句来源
     This combined model adopts the technique of neural network to fix weight coefficients,which gives better imitation of complex nonlinear system and offers a new effective solution of time serials data mining.
     该组合模型采用神经网络技术来确定权重系数,提高了对复杂非线性系统的拟合能力,为时间序列数据挖掘提供了一种新的实用方法.
短句来源
  “序列数据挖掘”译为未确定词的双语例句
     TSMiner,a model for time series visual data mining based on wavelet has been prosposed.
     论文提出一个基于小波分析的时间序列挖掘模型TSMiner,它支持时间序列数据挖掘的整个过程。
短句来源
     A Model Based on Wavelet Analysis for Data Mining on Time Series
     基于小波分析的时间序列数据挖掘模型
短句来源
     Time series similarity- pattern mining is a valuable one that extracts similar rules from time series data.
     在时间序列数据挖掘中有一类挖掘是把时间序列数据中相似规律发掘出来,这类挖掘就是相似模式的挖掘。
短句来源
     Data mining is one of hotspots of computer science. Time-Series mining is an important part of data mining research. Compared with other mature parts (such as association mining) of data mining, time-series mining is a newer direction.
     数据挖掘是当前计算机技术研究的热点之一,时间序列数据挖掘是数据挖掘研究的一个重要的组成部分,相对于数据挖掘较成熟的部分而言(比如关联规则的挖掘),时间序列数据挖掘的研究是数据挖掘较新的一个方向,其中多维时间序列的数据挖掘更是比较前沿的一个课题。
短句来源
     Study on Time Series Similarity and Trend Prediction
     时间序列数据挖掘中相似性和趋势预测的研究
短句来源
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  series data mining
While most time series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for an index structure that can support multiple distance measures.
      
In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices.
      
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
      
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
      
Motifs are useful for various time-series data mining tasks.
      
更多          
  sequential data mining
The combination of objective measurements and human perceptions using hidden Markov models with particular reference to sequential data mining and knowledge discovery is presented in this paper.
      
To refine the data mining process, this approach introduces the use of separate antecedent and consequent inclusion constraints, in addition to the traditional frequency and support constraints in sequential data mining.
      
A major problem with sequential data mining techniques is bottlenecks in the data access.
      
Data access, the major bottleneck for for sequential data mining, can be overcome without the use of a cluster.
      
Sequential Data mining can be confined to the data within a specified time frame.
      


In the process of data mining on time series, continuous time series needs to be transformed into discrete symbolic sequences. This paper proposes a simple but very efficient method for the symbolization of time series. This method has two features: First, it segments the time series by an approach called linear segmentation, which is well known for its shape expression and segmentation. Secondly it implements the online clustering analysis on the segmented time series with a fuzzy clustering algorithm using...

In the process of data mining on time series, continuous time series needs to be transformed into discrete symbolic sequences. This paper proposes a simple but very efficient method for the symbolization of time series. This method has two features: First, it segments the time series by an approach called linear segmentation, which is well known for its shape expression and segmentation. Secondly it implements the online clustering analysis on the segmented time series with a fuzzy clustering algorithm using neural network. The measure of shape similarity is also proposed in this paper. Effectiveness of this method has been verified by the symbolization of sample financial time series.

针对时间序列的数据挖掘首先需要将时间序列(Time Series)数据转换为离散的符号序列(Symbol Sequences),本文提出了一个简单高效的时间序列符号化方法,该方法的特点:一是利用线性化分段表示法所独有的形态分割与表达能力实现了时间序列的分段与表示;二是利用神经网络模糊聚类算法实现了时间序列的在线聚类。提出以矢量间形态相似性度量作为聚类依据。并利用该方法实现了对金融领域时间序列数据的符号化转换。

This paper proposes time series data mining strategy based on a rough set. It mainly discusses the acquisition of time-dependent and time-independent information from time series data. Practice proves that rough set theory, as an effective tool to deal with vagueness and uncertainty, is also effective to the time series data mining. Different information, such as information from original data, variation and variant ratio of data, is emphasized in the mining process of time series data.

阐述了基于 Rough集的时间序列数据的挖掘策略 ,重点讨论了时间序列数据中的时序与非时序信息的获取问题 .实践证明 ,Rough集理论作为一种处理模糊和不确定性问题的有效工具 ,对于时间序列数据的挖掘同样也是有效的 .文章强调了时间序列数据中的多方面信息 ,包括原始数据及其变化量、变化率所提供的信息

According to the developing law of matter, this article aims to put forward an approach of data mining to the sequence of time with the help of Autoregression Moving Average, and analyse the tendency.

根据事物发展的规律 ,利用自回归模型AR(n) ,根据拐点形态 ,提出一种时间序列的数据挖掘方法 ,并以此进行趋势分析 .

 
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