论文标题
您是您使用的：在物联网环境中基于用法的分析
You Are What You Use: Usage-based Profiling in IoT Environments
论文作者
论文摘要
习惯提取对于在智能家庭环境中自动化服务并提供设备使用见解至关重要。但是，习惯提取在查看特定活动的典型开始和结束时面临许多挑战。本文介绍了一种新颖的方式，可以使用无监督的聚类技术合奏来识别习惯。我们使用不同的聚类算法根据它们的静态或动态来提取习惯。轮廓系数和新型的噪声指标可适当提取习惯。此外，我们将提取的习惯与时间间隔联系起来，并具有信心评分，以表示当时可能会发生习惯的信心。
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.
