This paper introduces two construction algorithms of Classification decision tree based on parallel algorithm, and analyzes applicability.

本文重点介绍了两种基于并行算法的分类决策树的构造算法，并对它们的适用性及特点作了分析。

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In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.

提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。

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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.

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。

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