提出的可拓数据挖掘模式，有利于利用现存数据更好地为决策服务。
The extension data mining model, which is given in this article, is favorable to using existing data for making decision.
不过，回过头来看看本文的开头部分，我们知道数据挖掘绝不是仅仅是为了输出一个数值：它关乎的是识别模式和规则。
However, looking back to the top of the article, data mining isn't just about outputting a single number: It's about identifying patterns and rules.
此领域与数据挖掘密切相关，并且经常需要使用各种技巧，包括统计学、概率论和模式识别等。
The field is closely related to data mining and often USES techniques from statistics, probability theory, pattern recognition, and a host of other areas.
另外，原始的数据将向公众公开，其他的研究人员也可以挖掘隐藏的模式，作出自己的结论。
In addition, the raw data is likely to be released into the public domain, so that other researchers can dig for hidden patterns and make their own conclusions.
在间接的数据挖掘中，您会尝试创建数据组或找到现有数据内的模式—比如，创建“中产阶级妇女”的人群。
In undirected data mining, you are trying to create groups of data, or find patterns in existing data - creating the "Soccer Mom" demographic group, for example.
另外一方面，如果您要处理大量数据集(数据挖掘或数据库操作)，访问更大的数据缓存，那么对于64位模式来说这非常容易。
On the other hand, if you're working with a huge data set (data mining, or database operations), having access to a much larger data cache may quite easily make up for this.
数据挖掘是一个这样的过程，即向数据应用算法以揭露匹配给定上下文或查询的模式。
Data mining is the process of applying algorithms to data to uncover patterns that match a given context or query.
数据挖掘，就其核心而言，是指将大量数据转变为有实际意义的模式和规则。
Data mining, at its core, is the transformation of large amounts of data into meaningful patterns and rules.
在本文中，您复习了XML在数据挖掘中的用途和作用，包括模式匹配、变化监测、相似度搜索和监测、数据注释和语义。
In this article, you reviewed the use and roles of XML in data mining, including pattern matching, change detection, similarity search and detection, data annotation, and semantics.
BI利用数据挖掘之类的技术去析取和识别模式，并在大规模数据中进行修正。
BI USES techniques such as data mining to extract and identify patterns and correlations in large amounts of data.
经销店期望通过寻找数据内的模式挖掘这些数据并使用群集来判断其客户是否有某种行为特点。
They are hoping to mine this data by finding patterns in the data and by using clusters to determine if certain behaviors in their customers emerge.
数据挖掘使专家、分析师和用户可以洞察大量数据集中存在的模式，并使之成为日常业务流程中的一部分。
Data mining enables experts, analysts and users to gain insight into patterns in large data collections and to incorporate them in every day business processes.
数据挖掘旨在使用统计方法、人工智能和标准的数据库管理技术等等，从大型数据集中抽取模式。
Data mining seeks to extract patterns from large sets of data using, among other things, statistical methods, artificial intelligence, and standard database management techniques.
这些数据，当挖掘后，倾向于集中于某些特定年龄组和特定颜色周围，方便用户快速判断该数据内的模式。
The data, when mined, will tend to cluster around certain age groups and certain colors, allowing the user to quickly determine patterns in the data.
数据挖掘过程向数据应用算法，以揭露匹配某个给定上下文或查询的模式。
Data mining processes apply algorithms to data to uncover patterns matching a given context or query.
与地震学中地震序列研究相比，将数据挖掘的应用拓展到地震预报中，通过序贯模式来研究广义地震序列。
Compared to traditional research on earthquake sequence in seismology, data mining is applied to earthquake prediction, and sequential pattern is used to earthquake sequences.
设计模式包含用于选择单个数据挖掘模型和输入表的图形设计图面，同时还包含用于指定预测查询的网格。
Design mode includes a graphical design surface used for selecting a single data mining model and input table, and a grid used for specifying the prediction query.
但由于这些日志审计数据量非常庞大，因此采用数据挖掘技术从中进行安全模式规则的提取。
However, as the amount of the log audit date is too large, we can apply data mining technology into security mode rule extraction.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
AFS理论已初步应用于数据挖掘，模式识别，故障诊断等领域。
AFS theory has been applied to data mining, pattern recognition and failure diagnosis.
文本数据挖掘也不同于常规意义上的数据挖掘，常规数据挖掘是在数据库中发现感兴趣的模式，而文本数据挖掘是从自然语言文本中发现模式。
The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts.
其次，分析了数据挖掘中所使用的关联规则和序列模式，对关联规则和序列模式的各种挖掘算法进行了比较。
Secondly, it analyzed association rule and sequence mode used in the process of data mining and compared the main algorithms of association rule and sequence mode.
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。
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.
数据挖掘，又称数据库中的知识发现，是指从大型数据库或数据仓库中提取隐含的、事先未知的、潜在有用的信息或模式。
Data Mining, also referred to as Knowledge Discovery from database, is to abstract the potential, unknown and useful information or pattern from the large database or data warehouse.
其三是使用数据挖掘技术中的序列模式挖掘技术获得产品使用情况和特殊规律的信息。
The third is finding the information of products use and special rules by using the sequence pattern mining in the Data mining technique.
该方法已经应用在模式识别、数据挖掘、系统辨识与控制等领域。
The method is widely used in pattern recognition, data mining, and system recognition and control.
粗糙集理论被广泛应用于人工智能、模式识别、数据挖掘和知识发现等领域。
Rough sets theory was used widely to artificial intelligence, pattern recognition, data mining and knowledge discovery etc fields.
还提出由过程模拟和数据挖掘为重要方法的知识发布模式。
The knowledge release mode is also studied, which is characterized by process simulation and data mining.
目前，已被广泛应用于模式识别、函数逼近、数据挖掘等领域。
Presently it has been widely used in pattern recognition, function approximation and data mine etc.
介绍了建立带钢板形缺陷模式识别的数据挖掘过程。
The flatness defect pattern recognition based on data mining technology was proposed.
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