An Intelligent Data Mining Framework for IoT-Generated Big Data

The Internet of Things (IoT) has emerged as a powerful paradigm, generating vast amounts of data through connected devices across various domains. This data, often referred to as IoT-generated big data, presents significant challenges in terms of storage, processing, and analysis. An intelligent data mining framework is proposed to address these challenges, enhancing the ability to extract meaningful insights from the data. The framework integrates various data mining techniques, including machine learning and deep learning algorithms, to perform tasks such as classification, clustering, and anomaly detection. The system is designed to handle the volume, variety, and velocity of big data generated by IoT devices. Experimental results demonstrate the framework's superior performance in terms of accuracy, scalability, and efficiency compared to existing solutions. This approach not only improves data processing capabilities but also provides real-time decision support in applications such as smart cities, healthcare, and industrial IoT. The proposed framework is a significant step towards making IoT data actionable, contributing to the optimization of resource management and operational decision-making.