Habit extraction is essential to automate services and provide appliance usage insights in the smart home environment. However, habit extraction comes with plenty of challenges in viewing typical start and end times for particular activities. This paper introduces a novel way of identifying habits using an ensemble of unsupervised clustering techniques. We use different clustering algorithms to extract habits based on how static or dynamic they are. Silhouette coefficients and a novel noise metric are utilized to extract habits appropriately. Furthermore, we associate the extracted habits with time intervals and a confidence score to denote how confident we are that a habit is likely to occur at that time.
翻译:在智能家庭环境中,摘取哈比特对于服务自动化和提供电器使用洞察力至关重要,然而,对特定活动的典型起始和结束时间而言,习惯提取带来了许多挑战。本文介绍了一种新颖的方法,利用一组不受监督的集群技术来识别习惯。我们使用不同的组合算法来根据它们如何静止或动态来提取习惯。使用硅湿系数和新的噪音计量法来适当提取习惯。此外,我们把摘取的习惯与时间间隔和信心分数联系起来,以表明我们对于届时可能出现一种习惯的信心。