A Hybrid Machine Learning Framework for Efficient IoT Data Mining

Abstract

The proliferation of Internet of Things (IoT) devices generates vast volumes of diverse data, presenting significant challenges for data processing and analysis. This research proposes a hybrid machine learning framework designed specifically to enhance the efficiency of IoT data mining. By integrating multiple machine learning algorithms, this framework harnesses the strengths of both supervised and unsupervised learning techniques to improve data accuracy and uncover meaningful insights from heterogeneous data sources. It employs advanced data processing techniques, including clustering, classification, and anomaly detection, to systematically handle the complexities of IoT environments. Moreover, the proposed framework addresses common issues such as data noise, variability, and scalability, ensuring robust performance in real-time applications. Through extensive experimentation and evaluation on diverse IoT datasets, the framework's efficacy in achieving high accuracy and lower computational costs is demonstrated. This research ultimately aims to provide a scalable, effective tool that not only enhances IoT data mining capabilities but also contributes significantly to decision-making processes across various sectors, including smart cities, healthcare, and industrial automation.