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随着智能家居行业的蓬勃发展,用户对智能家居所能提供的智能化服务需求越来越大.而现有的智能家居系统通常只能按照预先设定的控制方式和规则简单重复运行,不能根据用户的使用习惯来适时适度的推荐能够反映其个性化需求的控制策略.针对这种情况,采用主流数据挖掘方法来预测用户个性化行为.基于云合智能家居系统开展了相关实验,模拟了一年内10个家庭日常数据.经过对三种数据挖掘算法的对比实验,所采用的支持向量机算法能为不同用户提供符合其个性化需求的服务.
With the vigorous development of the smart home industry, the demand for smart services provided by smart home users is increasing, while the existing smart home systems can only be simply re-run according to preset control methods and rules. According to the user’s habits to timely and appropriate recommendation to reflect the individual needs of the control strategy.In view of this situation, the mainstream data mining methods to predict the user’s personalized behavior based on cloud-based intelligent home system to carry out the relevant experiments, simulated The daily data of 10 families in a year.After comparing the three data mining algorithms, the support vector machine algorithm can provide different users with services that meet their individual needs.