Abstract
The issue of comfort in school classrooms in Mexico has not been adequately addressed nor effectively managed with the available tools. This inadequacy stems from a lack of proper understanding regarding their implementation. This study conducts an analysis of the application of data mining techniques utilizing climatological variables within a classroom setting to identify patterns and behaviors that predict student comfort. A comparison of various data mining models is performed, and the most effective models are presented. The study employs a dataset encompassing variables such as Temperature, Humidity, Air Quality, Light, and Noise from a classroom environment. Patterns were identified and subsequently labeled into two clusters to develop a student comfort model. Two unsupervised learning models, hierarchical clustering and k-means, were utilized alongside supervised learning models including Neural Networks, Naive Bayes, Decision Trees, and K-Nearest Neighbors.
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Garcia, I.J., Fernandez-Dominguez, F., Melendez-Armenta, R.A., Muñoz-Benítez, J. (2025). Optimization of Student Comfort Using Ambient Intelligence and Data Mining. In: Guarda, T., Portela, F., Gatica, G. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024. Communications in Computer and Information Science, vol 2346. Springer, Cham. https://doi.org/10.1007/978-3-031-83210-9_4
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