In the follwing two chapters, a decision tree classification alortithm base on granule hierarchy was introduced.
在接下来的两章中，本文提出一种基于粒度层次的决策树分类算法。
参考来源 - 基于粒度层次的数据挖掘分类算法研究For the studying region of Beijing, multi-temporal TM images were selected for crop classification. The widely planted crops were classified through the algorithms of the decision tree classification based on the multi-temporal and multi-spectral images in Beijing region.
对于北京研究区选用了中高分辨率的 TM 图像,在时相和波谱信息融合的基础上利用决策树分类的方法对北京主要农作物进行了分类。
参考来源 - 基于时相和波谱信息的作物分类研究·2,447,543篇论文数据，部分数据来源于NoteExpress
In the end, decision tree classification experiments results and contrastive precision accuracy are obtained.
最后进行了分步决策树分类实验和与传统分类方法的精度对比分析。
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。
Meanwhile it describes the decision tree classification algorithm in detail, analyzes the ID3, C4.5 and other prevalent decision tree algorithm.
同时详细的阐述了决策树分类算法，并对比较流行的决策树算法id3、C4.5等算法进行详细分析与比较。
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