英语翻译根据原始数据样本中是否含有模式类别的信息,特征选择过程可分为有监督特征选择和无监督特征选择.有监督特征选择是指在
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According to the original data samples contain pattern categories of information, the feature selection process can be divided into supervised feature selection and unsupervised feature selection. Supervised feature selection is to point to in under the premise of the given pattern categories, using the characteristics and the relationship between the characteristics and categories to select feature set process. Unsupervised feature selection is to point to in the original data set, the relationship between the characteristics of their own through data set for feature selection. In this paper the characteristics of the user selection process, we adopt unsupervised feature selection method, based on experience judgement criterion, choose a suitable user data feature subset to best cover the natural classification of data. At present commonly used algorithm with feature selection method based on genetic algorithm [5], feature selection method based on pattern similarity judgment [6] and information gain method of feature selection [7], this algorithm did not consider the correlation between features and feature attributes affect classification. This article attributes to the terminal according to the customer preference influence on the result of the classification and correlation analysis between customer attributes two aspects, this paper proposes a k-means clustering are based on genetic algorithm and the customer property feature selection method, the method is based on unsupervised learning feature selection algorithm. The basic idea is to use genetic algorithm to choose the initial feature subsets, for each feature subset k-means clustering algorithm are used to determine the optimal class number, and then to the DB Index set a judgment function is used for feature selection criterion, finally from the selected feature subset deleted correlation characteristics, reduce redundancy.

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