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.