本文研究基于SLIQ的数据挖掘分类算法。
This paper studies data mining classification calculation of SLIQ.
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。
The application of data mining classification techniques in credit scoring involves the process of building and selecting optimal models.
本文讨论了两种数据挖掘算法：分类树和群集。
This article discussed two data mining algorithms: the classification tree and clustering.
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.
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。
Future articles will touch upon other methods of mining data, including clustering, Nearest Neighbor, and classification trees.
创建一个分类树(一个决策树)，并借此挖掘数据就可以确定这个人购买一辆新的M5的可能性有多大。
By creating a classification tree (a decision tree), the data can be mined to determine the likelihood of this person to buy a new M5.
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。
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.
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 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.
对于这类数据，分类树是一种极不适合的数据挖掘模型。
InfoSphereWarehouse还包含一个TaxonomyEditor，它可以把词典条目分类为分类法树，可以供数据挖掘和OLAP使用。
InfoSphere Warehouse also includes a taxonomy Editor that categorizes dictionary entries in a taxonomy tree for use in data mining and OLAP.
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。
The paper explores possible applications of Prior knowledge Support Vector Machine (P-SVM) based data mining algorithm in an automatic TCM syndrome classification system.
分类是数据挖掘领域中的一个重要研究课题。
随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
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.
分类是数据挖掘中的一种非常重要的方法。
Classification is one of important methods used in data mining.
分类是数据挖掘领域中一个重要的研究分支。
The Classification is an important research branch in the Data Mining domain.
从数据挖掘的观点来看，它们都与分类算法密切相关。
Those are tightly associated with classification algorithm on the view of data mining.
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。
This paper presents a classification method for data mining based on BP neural network, and puts forward improvement ideas.
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。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
一种新的抽样方法是把数据挖掘技术中的分类、聚类及离群点挖掘等应用到审计风险管理中去。
A new sampling method is proposed, which USES the latest technologies of database. It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
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。
This paper comprehensively introduces the concept, classification, task, methods and pricipal applications of data mining.
本文主要研究适合于大规模科学数据挖掘的分类和聚类的理论和应用。
The main point of this paper is to research the theories and applications of classifying and clustering which is suitable for large-scale science data mining.
文本分类是数据挖掘的重要课题，它是获取信息资源的重要方式之一。
Text categorization is an important task in Data Mining, and it is an important way for getting information.
决策树是分类数据挖掘的重要方法。
Decision tree is one of the important Categorising Data Mining methods.
文本分类是文本数据挖掘的重要技术。
Text categorization is one of the important techniques in textual 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。
Data classification is an important task of data mining, and developing high-powered classification algorithm is one of the key problems for 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 the most basic tasks during the biological statistics and data mining.
面对大规模、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘研究的重要方向之一。
Facing the massive volume and high dimensional data, how to build effective and scalable algorithm for data mining is one of research directions of data mining.
面对大规模、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘研究的重要方向之一。
Facing the massive volume and high dimensional data, how to build effective and scalable algorithm for data mining is one of research directions of data mining.
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