IoT Activity Symphony: Harmonizing DBSCAN Clustering and t-SNE Visualization for Enhanced Recognition.

This study introduces the IoT Activity Symphony Framework, a new algorithm that combines t-SNE with DBSCAN to cluster data from the Internet of Things. Using a variety of datasets obtained from Kaggle, the system outperforms previous research by a wide margin, earning a Silhouette Score of 0.72 and a Davies-Bouldin Index of 1.08. This model is a huge step forward for IoT analytics thanks to its flexibility, optimised hyperparameters, and strong clustering abilities. In particular, the results of our comparison demonstrate the framework's competence in dealing with real-world situations, yielding useful information for anomaly identification and predictive maintenance. The study backs up the suggested technique and presents the framework as a useful resource for academics and professionals working with data from the Internet of Things. With the introduction of a novel clustering technique, this study adds to the developing area and demonstrates the promising future of practical applications in many IoT fields. Additional clustering techniques, real-time capabilities, and assessments in a variety of IoT application scenarios are also potential directions for future study. An encouraging development is the IoT Activity Symphony Framework, which opens the door to better comprehension and use of complicated IoT statistics.