Abstract
Predictions in data mining are a difficult process but useful in various areas. The purpose of this article is to make a parallel between the classical data mining process and two new approaches in the process of data mining: collaborative data mining and context-aware data mining. Data gathered from seven meteorological stations in Transylvania served as baseline for the research. Processes for predicting the air humidity were designed and analyzed using the same machine learning algorithms and data. The results obtained prove that collaborative and context-aware data mining approaches bring better results than the standalone approach and highlight some of the algorithms that are more suitable for each approach. The combination of the two notions could be another example of a successful approach for future research.
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Anton, C.A., Avram, A., Petrovan, A., Matei, O. (2019). Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_5
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