This paper studies data mining classification calculation of SLIQ.
本文研究基于SLIQ的数据挖掘分类算法。
The application of data mining classification techniques in credit scoring involves the process of building and selecting optimal models.
运用数据挖掘分类技术于信用评分问题包括一个建立模型以及选择最优模型的过程。
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.
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。
Classification (also known as classification trees or decision trees) is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance.
分类(也即分类树或决策树)是一种数据挖掘算法，为如何确定一个新的数据实例的输出创建逐步指导。
This article discussed two data mining algorithms: the classification tree and clustering.
本文讨论了两种数据挖掘算法：分类树和群集。
Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.
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。
Future articles will touch upon other methods of mining data, including clustering, Nearest Neighbor, and classification trees.
本系列后续的文章将会涉及挖掘数据的其他方法，包括群集、最近的邻居以及分类树。
The new data has no classification (in this case, no checks on heart disease have been made) and the scoring process assigns a prediction to each new record according to the mining model.
新的数据没有分类别（这里是指还没有做过心脏病检查），评价过程根据挖掘模型将一个预测赋给每个新的记录。
The paper explores possible applications of Prior knowledge Support Vector Machine (P-SVM) based data mining algorithm in an automatic TCM syndrome classification system.
目的：探讨带先验知识的支持向量机（P-SVM）数据挖掘算法在中医证候信息自动分类中的应用。
Decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm, the experiment results are given.
利用决策树算法对乳腺癌图像数据进行分类，实现了一个基于决策树算法的医学图像分类器，获得了分类的实验结果。
Classification is an important problem in data mining.
分类是数据挖掘领域中的一个重要研究课题。
Data classification is one of important contents from Data mining.
数据分类是数据挖掘中一个重要的内容。
Classification is one of the most basic tasks during the biological statistics and data mining.
在生物统计以及数据挖掘中，分类预测是最基本的任务之一。
Classification is one of important methods used in data mining.
分类是数据挖掘中的一种非常重要的方法。
Those are tightly associated with classification algorithm on the view of data mining.
从数据挖掘的观点来看，它们都与分类算法密切相关。
This paper presents a classification method for data mining based on BP neural network, and puts forward improvement ideas.
本文介绍一种基于BP神经网络的数据挖掘的分类方法，并提出了改进思想。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
在数据挖掘中存在多种算法，决策树分类算法是应用比较多的一种。
This thesis applies data mining techniques to customer segmentation based on customer value matrix and builds the classification model of customer with different value.
本文把数据挖掘技术应用于基于客户价值矩阵的客户价值细分中，建立各类价值客户的分类模型。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
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。
Data mining always faces complicated tasks that including classification, prediction, association rule discovering and clustering, etc.
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。
This article mainly refers to the basic concept, classification and process of web data mining, as well as its application in e-commerce.
主要介绍网络数据挖掘的基本概念、分类、挖掘的过程及其在电子商务中的应用。
The thesis also examines the rough set model based on classification accuracy. The MIE-RS data mining approach given later is based on the model.
另外，作者提出了基于分类正确度的粗糙集模型，该模型已用于作者研制的数据挖掘方法MIE-RS上。
It is a necessary part of data mining of data pretreatment that cleaning and inducing data and providing object data for classification algorithm.
数据预处理是数据挖掘中不可或缺的一部分，是对数据进行初步地清理和归纳，为分类算法提供目标数据。
In the second and third chapters it introduces data mining and related problems, including the process, method, classification and application of data mining.
在第二章我介绍了数据挖掘及其相关问题，包括数据挖掘的过程、方法、分类和应用等。
This paper comprehensively introduces the concept, classification, task, methods and pricipal applications of data mining.
本文综合介绍了数据挖掘的概念、分类、任务和方法，并展示了其丰富的应用领域。
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.
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 and clustering are both commonly used data mining methods. The advantage of classification is that the accuracy is higher, but the labeled training set is needed.
分类和聚类都是常用的数据挖掘方法，分类的优点是准确率较高，但需要带有类别标注的训练集；
As one of the kernel techniques in the data mining, it is necessary to summarize the research status of classification algorithm.
对数据挖掘中的核心技术分类算法的内容及其研究现状进行综述。
Clustering analysis is one of the basic methods of the data mining and knowledge finding and it is a non - surveillance data classification method.
聚类分析是在无先验知识无指导下进行数据无监督分类的一种数据挖掘技术。
Data classification is an important task of data mining, and developing high-powered classification algorithm is one of the key problems for data mining.
数据分类是数据挖掘中的一个重要课题，研究各种高效的分类算法是数据挖掘的重要问题之一。
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