Exploring Unsupervised Learning Techniques for the Internet of Things
Giampaolo Casolla, Salvatore Cuomo, Vincenzo Schiano Di Cola, Francesco Piccialli · IEEE Transactions on Industrial Informatics · 2019
Nowadays, machine learning (ML) techniques can provide new perspectives to identify hidden patterns and classes inside data. Applying ML to the Internet of Things (IoT) and its produced data represents a great challenge in every application domain, since analyzing IoT data increasingly requires the use of advanced mathematical algorithms, novel computational techniques, and services. In this article, we present and discuss the application of unsupervised learning techniques on IoT data collected in a cultural heritage framework. Behavioral data have been gathered in a noninvasive way in order to achieve an ML classification that can be exploited by cultural stakeholders in terms of the medium- to long-term strategy and also in terms of strictly operational decisions. The application of ML and other learning techniques will acquire a key role to complement the more traditional services with new intelligent ones able to satisfy the needs of companies, stakeholders, and consumers.