Research on time series data mining is one of important hot spots of data mining.
目前时间序列的数据挖掘是数据挖掘的重要研究热点之一。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
This hybrid model synthesizes the merits of multiple intelligent computation methods and offers a new effective solution of time series data mining.
该混合模型融合多种智能计算方法优点于一体，为时序数据挖掘提供了一种新的实用方法。
A way of time series data mining was put forward based on the exploratory analysis and the mathematics module was founded by way of using linear regression technology.
提出了基于探索性分析的时序数据挖掘方法，采用线性回归技术建立了数学模型。
While as the particularity of data description, researchers pay much attention to how to apply the traditional data mining technologies to time-series data mining and forecasting.
由于数据描述的特殊性，如何把传统的数据挖掘技术应用于时间序列的挖掘与预测中更加受到国内外学者的广泛关注。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
In the previous two articles in this "data mining with WEKA" series, I introduced the concept of data mining.
在这个“用WEKA进行数据挖掘”系列之前的两篇文章中，我介绍了数据挖掘的概念。
The upcoming articles of this series show how to do more powerful things with drill-through definitions, such as invoking data mining dynamically.
本系列的后续文章将展示如何使用穿透钻取定义完成一些更复杂事情，比如动态调用数据挖掘。
Tune in to this series to learn about distributed data mining next month.
请在下个月继续关注本系列以了解分布式数据挖掘。
Throughout the series, you discover how to manage and organize data and content, learn about distributed data mining, and find tips for analyzing and presenting information to users.
在本系列中，您将了解如何管理和组织数据与内容，了解分布式数据挖掘并学习分析信息并向用户呈现的一些技巧。
This article wraps up the three-article series introducing you to the concepts of data mining and especially to the WEKA software.
本文是由三篇文章组成的系列文章的终结篇，该系列向您介绍了数据挖掘的概念尤其是WEKA软件。
Therefore, this series of articles will only scratch the surface of what is possible with data mining.
因为本系列只触及能用数据挖掘实现的功能的一些皮毛。
Finally, I want to reiterate that this article and the ones in the future parts of this series only are a brief introduction to the field of statistics and data mining.
最后，我再重申一下，本文及本系列的后续文章只是对数据统计和数据挖掘领域做了最简单的介绍。
Part 3 will bring the "Data mining with WEKA" series to a close by finishing up our discussion of models with the nearest-neighbor model.
第3部分是“用WEKA进行数据挖掘”系列的结束篇，会以最近邻模型结束我们对模型的讨论。
Our goal is to explore the open source tooling available for a beginner and to foster an appreciation for the value that data mining might provide. Keep that in mind as we continue the series.
我们的目的就是让初学者充分领略这个可用的开源工具的妙处并提高对数据挖掘所能提供的价值的了解和重视。
Linked data, semantic analysis, analytics and data mining all form a layer on top of the content-web that could serve as the foundation for the next series of applications and other added value.
关联数据、语义分析、分析数据挖掘，这些都可以作为下一代网络产品和其它附加值的基础。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
By recurring to data Mining, the analysts can use a series of tools to analyze the data, get the feedback, and review the raw information in the new Angle of view.
通过这一技术，人们在计算机帮助下利用一系列工具对数据进行分析，然后根据反馈的内容从新的视角来考察原始数据的信息。
Based on the project background, an improved outlier data mining algorithm for time series data is given out.
根据课题背景，给出一个针对时序数据的离群数据挖掘算法的改进算法。
As one of the important forms of complex data, time series is a hotspot in data mining area.
作为一类重要的复杂类型数据，时间序列已成为数据挖掘领域的热点研究对象之一。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
Data mining are used to analyze the foreign exchange rate time series and acquire the correct, implicated and hidden information, which has practical significance in the financial field.
利用数据挖掘技术分析外汇汇率时间序列，从时间序列中获得正确的、隐含的、潜在的信息对于金融领域研究具有重要的现实意义。
As a very common type of the data sets, time series has been one of the focuses of the current data mining research.
时间序列数据在数据库数据中十分普遍，于是对时间序列进行数据挖掘已成为当前研究的焦点之一。
Opposite to mature part of data mining (such as mining of database association rules and classify rules), mining of time series still falls into a new branch.
目前对于时间序列数据挖掘的研究主要集中在相似性搜索和模式挖掘上。
Mining similar sequences and similar trends in time-series databases is a novel and important problem in data mining literature.
时间序列数据库中相似序列与相似趋势的挖掘，是数据挖掘领域的一个较新的重要问题。
Focusing on the problem of data mining in time-series, did research in transforming time-series to trend sequences and methods of performing data mining in acquired trend sequences.
本文针对时间序列的数据挖掘问题，研究了将时间序列转化为趋势序列，以及趋势序列中的数据挖掘问题。
Based on above analysis, this paper integrates the study of data mining and financial time series.
基于上述原因，本文将数据挖掘和金融时间序列结合在一起进行研究。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
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