Research on Methods and Techniques for IoT Big Data Cluster Analysis

With the rapid development of Internet of Things technology, there have been many applications related to the Internet of Things. The "Internet of Things" and "big data" have become a closely related field of application of technology. How to effectively find valuable model relationships from big data in the Internet of Things is conducive to managers making correct decisions on the company's future development trends, and at the same time it is also conducive to improving corporate profits. After introducing the concept of complex event relations, the big data processing of the Internet of Things has been transformed into the extraction and analysis of complex relationship patterns, thus providing support for simplifying the processing complexity of Internet of Things big data. The traditional K-means algorithm is optimized to make it suitable for the needs of Big Data RFID Internet of Things data. Based on the Hadoop cloud clustering platform, K-means clustering analysis is implemented. Based on the traditional clustering algorithm, the center point selection technology suitable for RFID Internet of Things data clustering is selected, so that the clustering efficiency is improved, and a design and implementation is realized.