Decision tree is an important method for data mining as well as induction learning.
决策树是数据挖掘和归纳学习的重要方法。
Decision tree is an important method in induction learning as well as in data mining, which can be used to form classification and predictive model.
决策树是归纳学习和数据挖掘的重要方法，通常用来形成分类器和预测模型。
Decision tree induction is a kind of the induction learning.
决策树归纳是归纳学习的一种。
Simplifying trees is the key part of decision tree induction learning.
树的简化是决策树归纳学习中关键的部分。
This paper proposes a new algorithm called LAID(learning algorithm as inverse deduction) considering the difficulty of traditional induction learning.
针对传统归纳学习的困难，提出一种新的逆演绎的学习算法。
Most data mining and induction learning methods can only deal with discrete attributes; therefore, discretization of continuous attributes is necessary.
很多数据挖掘方法只能处理离散值的属性，因此，连续属性必须进行离散化。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge.
基于ID 3算法的决策树归纳学习是归纳学习的一个重要分支，可用于知识的自动获取过程。
Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge.
基于ID 3算法的决策树归纳学习是归纳学习的一个重要分支，可用于知识的自动获取过程。
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