However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.

但是，无指导学习环境下的属性选择往往无法取得像有指导学习环境下那样令人满意的结果。

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According to various of applications of the datasets, feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches.

属性选择问题可以分为有指导学习环境下的选择和无指导学习环境下的选择。

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The method of product feature extraction and analysis can be divided into supervised machine learning methods, semi-supervised machine learning algorithms and unsupervised machine learning algorithm.

产品评价对象的提取与分析的方法主要分为有监督的机器学习方法、半监督的机器学习算法、无监督的机器学习算法。

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