最后，选择规则的同步融合策略实现多数据源中的分类规则挖掘。
Then the method of synchronous amalgamating is chosen to implement the mining of classification rules from multiple data sources.
本文采用一种基于蚁群算法的分类规则挖掘算法，其特征实质上是一种序列覆盖算法。
The paper proposed an algorithm based on ant colony algorithm for mining classification rule from the Student Scores Management Database.
通过训练数据的分析阐述了分类规则挖掘的方法，初步的实验结果验证了该方法的有效性。
Using the training data, it explains the method of mining classification rules, and abecedarian experiment results validate its effectiveness.
实验表明，基于混合遗传算法的分类规则挖掘方法能够从数据集中发现一个简洁、准确、易理解的规则集。
Experiment shows that the classification rule mining method using hybrid genetic algorithms can find a set of the succinct, accurate and comprehensible classification rules.
数据挖掘通常涉及到一些标准的任务，包括聚集、分类、回归分析和关联性规则学习。
Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.
然后对于挖掘到的策略和规则需要进行分类以便确定业务敏捷性，这样可能就会产生一个假的业务敏捷策略指令工作组。
The mined policies and rules will then have to be classified identifying business agility and it may be possible to derive a strawman for a working set of business policy directives.
各种模式各有侧重，其中有一些已经研究得较为成熟，研究成果也较多，如挖掘关联规则、预测方法和分类模式中的一些其他方法。
Each mode has its own emphasis, among them, there are some already studied modes have much more research outcome, such as some methods in association rule mining, classification and forecast mode.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
数据挖掘面对的任务是复杂的，通常包括分类、预测、关联规则发现和聚类分析等。
Data mining always faces complicated tasks that including classification, prediction, association rule discovering and clustering, etc.
通过分类正确度有效处理了决策表的不一致性，采用启发式算法，挖掘出满足给定精确度的最简产生式规则知识。
We deal with the inconsistency through classification accuracy, using heuristic algorithms we can get a set of minimal productive rules satisfying the given classification accuracy.
关联规则挖掘可以帮助许多商务决策的制定，如分类设计、交叉购物和贱卖分析。
Association rule mining may help making many business decisions such as catalog design, cross-marketing, and loss-leader analysis.
它包含关联规则挖掘、预测、分类、聚类、演化分析等多种技术手段。
It includes lots of measures such as association rules mining, classification and prediction, clustering analysis and evolvement analysis.
数据挖掘是知识发现领域的一个重要问题，粗糙集理论是一种具有模糊边界的数据挖掘方法，它被广泛应用于决策系统的分类规则提取中。
Data mining is an important problem in KDD, and Rough set as a theory of set with fuzzy boundary is widely applied to infer classification rules from decision system.
大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。
Most data mining tools use rule discovery and decision tree technology to extract data patterns and rules; its core is the inductive algorithm.
分类是数据挖掘的一项重要任务，如何发现可理解的、令人感兴趣的分类规则是数据挖掘面临的一个主要问题。
Classification is an important task in data mining field, how to discover the intelligible and interesting classification rules is one of the main problems facing data mining.
数据挖掘是本课题的研究核心，主要包括关联规则发现、数据聚类和数据分类。
Data mining is the core topic of this paper. Basically, it includes associate rule founding, data clustering and data assorting.
为了提高决策系统的分类质量，探讨了一种在数据仓库中基于粗糙逼近近似度量的挖掘分类规则策略。
In order to improve the classification quality of decision system, a strategy of data mining classification rules based on rough approaching approximation measurement in data ware is proposed.
本文对时间序列模式、分类规则和关联规则挖掘的方法进行了深入的研究。
In this thesis, the thorough study of time serial model, classification rule and association rule is made.
对常用于入侵检测系统中的数据挖掘技术如关联规则，序列分析，分类分析等进行了分析。
It also discusses some techniques of data mining such as mining association rules, mining sequential patterns and data classification.
然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；
Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.
时间序列模式、分类规则和关联规则挖掘是当前数据挖掘研究中一个热点。
It is a hotspot that the data mining of time serial model, classify rule, association rule in the data mining study currently.
用于知识发现的大部分数据挖掘工具均采用规则发现和决策树分类技术来发现数据模式和规则。
Most data mining tools for knowledge discovery generally use rule discovery and decision tree technology to extract data patterns and rules.
分类规则发现则是通过对训练样本数据集的学习构造分类规则的过程，是数据挖掘、知识发现的一个重要方面。
Classification rules discovery is a procedure to construct a classifier through studying the training dataset. It is a very important part of data Mining and Knowledge discovery.
然后，在此基础上提出一种改进的模糊关联算法挖掘分类关联规则；
Secondly, an improved fuzzy association method was proposed to mine the classification association rules.
特别是分类关联规则既能用于概念描述又能用于分类预测与决策，在数据挖掘中发挥重要作用。
In particular, the class association rule (CAR), which can be used not only for concept description but also for class prediction and decision-making, has an important role in data mining.
将遗传程序设计方法应用到分类规则的挖掘上，利用具有分层结构的两类别分类问题的分类器解决多类别的分类问题。
Genetic Programming is applied to mining classification rules, it can effectively solve multiclass classification by several two-class classifiers using structural hierarchy.
从某种意义上说，通过粗集理论挖掘出的分类规则是系统通过自学习机制而产生的，因而可以解决知识自动获取的瓶颈问题。
In a sense, rough sets is a kind of self-study mechanism, so we can solve the problem of knowledge obtained automatically by using rough sets.
从某种意义上说，通过粗集理论挖掘出的分类规则是系统通过自学习机制而产生的，因而可以解决知识自动获取的瓶颈问题。
In a sense, rough sets is a kind of self-study mechanism, so we can solve the problem of knowledge obtained automatically by using rough sets.
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