An association rules data mining method based on the theory of multi-objective optimization soft subspace clustering is proposed to mine the data feature of local discrete text data in high-dimensional dataset effectively. The thought of multi-objec-tive optimization soft subspace clustering is combined with the theory of non-dominated sorting genetic optimization to optimize the weighted intra-class compactness and weighted inter-class separation function,and obtain the optimized objective function and non-dominated Pareto optimal solution set. The weighting subspace classification method is used to cluster the features of the optimal solution set. A recognition method for feature extraction and text association based on the thought of association rules is used to recognize and classify the features among texts and within texts for the clustered text features,which can realize the ef-fective mining of the text information data. The experimental results show that the data mining method of multi-objective optimiza-tion soft subspace clustering can realize the local discrete text data mining in high-dimensional dataset effectively.%提出利用基于多目标优化软子空间聚类理论的关联规则数据挖掘方法对高维数据集中局部离散文本数据实现数据特征有效挖掘.首先,利用多目标优化软子空间聚类思想结合非支配排序遗传理论优化加权类内紧致及加权类间分离函数,获取优化后的目标函数及非占优Pareto最优解集,运用加权子空间划分方法对最优解集完成特征聚类;其次,基于关联规则思想运用一种特征提取和关联文本的识别方法,对聚类后的文本特征进行文本间及文本内部的特征识别和分类,即实现了文本信息数据的有效挖掘.实验证明,利用多目标优化软子空间聚类数据挖掘方法可以有效实现高维集中局部离散文本数据的挖掘.
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