Nowadays, unsupervised learning can provide new perspectives to identify hidden patterns and classes inside the huge amount of data coming from the Internet of Things (IoT) world. Analyzing IoT data through machine learning techniques requires the use of mathematical algorithms, computational techniques, and an accurate tuning of the input parameters. In this article, we present a study of unsupervised learning techniques applied on IoT data to support decision-making processes inside intelligent environments. To assess the proposed approach, we discuss two case studies in which behavioral IoT data have been collected, also in a noninvasive way, in order to achieve an unsupervised classification that can be adopted during a decision-making process. The use of unsupervised learning techniques is acquiring a key role to complement the more traditional services with new decision-making ones supporting the needs of companies, stakeholders, and consumers.

中文翻译：

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通过无监督学习在物联网环境中做出决策

如今，无监督学习可以提供新的视角来识别来自物联网 (IoT) 世界的大量数据中隐藏的模式和类别。通过机器学习技术分析物联网数据需要使用数学算法、计算技术和输入参数的准确调整。在本文中，我们将研究应用于物联网数据的无监督学习技术，以支持智能环境中的决策过程。为了评估所提出的方法，我们讨论了两个案例研究，其中也以非侵入性方式收集了行为物联网数据，以实现可在决策过程中采用的无监督分类。