决策树分类器 meaning in Chinese
decision tree classifier
Examples
- In this paper , based on the comprehension of the current research situation , we mainly discussed the problem how to adapt the decision tree in common use to the large scale dataset
针对大数据量、多属性值的情况，对决策树分类器所需属性信息的求解提出了新的改进算法。 - On the basis of analyzing the classification principle of decision tree classifier and parallelpiped classifier , a new classification method based on normalized euclidian distance , called wmdc ( weighted minimum distance classifier ) , was proposed
通过分析多重限制分类器和决策树分类器的分类原则，提出了基于标准化欧式距离的加权最小距离分类器。 - A decision tree classifier using a scalable id3 algorithm is developed by microsoft visual c + + 6 . 0 . some actual training set has been put to test the classifier and the experiment shows that the classifier can successfully build decision trees and has good scalability
最后着重介绍了作者独立完成的一个决策树分类器。它使用的核心算法为可伸缩的id3算法，分类器使用microsoftvisualc + + 6 . 0开发。 - Data warehouse is a hot research area in 90s its main motif is to provide the decision - maker a powerful tool : gathering the data in pure consistent , relevant pattern , and making use of the data in managing analyzing , data - mining purposec that means that the decision - maker can use the tool to understand , grasp the situation of the business from different directions and forecast the future of it when using data warehouse , the processing speed determines data warehouse ' s practicability and processing ability the hoc ( highway decision center ) system realized before solves some key problems about intermediate scale data , mainly concentrating data warehouse performance coefficient when using hdc in large scale data , it encountered processing speed problem then the settlement of this problem becomes a major research point so , based on the former research achievements , the present task is to construct the renowned data warehouse architecture and its relevant algorithms , then adapts the system to the large scale dataset with data mining functions c this paper is a part of the research in order to construct the powerful system , a key problem is to cope with the processing - speed problem and the data space problem , etc , - caused by the large scale dataset and magnificent dataset this is also the core in the present data mining research this paper ' s motive is to design and realize a decision - tree classifier in the data warehouse system for large - scale dataset
大型数据仓库的处理速度问题目前是制约其推广应用的关键所在，也是这一领域的一个重要研究课题，也正是我们当前工作的重点：在前期研究工作的基础上围绕提高大型数据仓库处理速度问题，建立改进的数据仓库系统模型和相关算法，开发出面向中级以上企事业单位的、具有数据挖掘和分析能力的大型数据仓库系统。建立大型数据仓库所面临的关键问题，是如何妥善解决实际业务数据的大规模、海量特征所带来的处理速度和空间等问题，这也是当前挖掘技术研究必然面对的核心问题。本研究的目的是设计并实现大型数据仓库系统中的分类数据挖掘工具? ?决策树分类器，主要工作是在综合了解现有决策树分类算法的研究情况的前提下，对决策树算法适应大规模数据集的问题进行探讨，力求设计出能较好地适应大规模数据的分类器算法。 - A modified k - means method which can reduce compute complexity greatly is proposed to cluster similar customers . churn prediction adopts decision trees algorithms . after presenting a brief overview of tree - building algorithm and tree - pruning algorithm of traditional decision trees , the paper describes how to push constraints into the tree - building phase and tree - pruning phase in detail
离网预测采用了决策树分类器，本文在描述决策树算法中所涉及到的建树、代价计算、剪枝等问题之后，给出了在建树中和建树后分别加入限制条件的修剪算法。