Exploring the Efficacy of Data Mining Algorithms for IoT Data Analysis

The advent of the Internet of Things (IoT) has ushered in an era of unprecedented data generation, offering opportunities for enhanced decision-making, automation, and efficiency across various domains. This paper investigates the applicability of prominent data mining algorithms for analysing IoT data, aiming to understand their effectiveness and efficiency. Through a preliminary analysis of real IoT datasets, including Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), C۴.۵, C۵.۰, Artificial Neural Networks (ANNs), and Deep Learning ANNs (DLANNs), we evaluate their performance in terms of accuracy, computational efficiency, and suitability for IoT applications. Initial findings suggest that while certain algorithms exhibit superior accuracy, others offer advantages in terms of computational efficiency. These insights contribute to a deeper understanding of the role of data mining algorithms in harnessing the potential of IoT data.

کلیدواژه ها:

Internet of Things (IoT) ، Support Vector Machine (SVM) ، K-Nearest Neighbours (KNN) ، Linear Discriminant Analysis (LDA) ، Nä۱ve Bayes (NB) ، C۴.۵ ، C۵.۰ ، Artificial Neural Networks (ANNs) ، Deep Learning ANNs (DLANNs) ، Data Mining ،