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Explores Association Rule Mining, emphasizing Frequent Itemsets and Alternative Measures of Interest, including the FP-Growth algorithm and performance comparison.
Delves into the energy-efficient distributed machine learning approach in the IoT era, emphasizing the importance of summarizing data for improved communication energy efficiency.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.