An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets

With the rapid development of artificial intelligence, various medical devices and wearable devices have emerged, enabling people to collect various health data of themselves in hospitals or other places. This has led to a substantial increase in the scale of medical data, and it is impossible to import these data into memory at one time. As a result, the hardware requirements of the computer become higher and the time consumption increases. This paper introduces an online clustering framework, divides the large data set into several small data blocks, processes each data block by weighting clustering, and obtains the cluster center and corresponding weight of each data block. Finally, the final cluster center is obtained by processing these cluster centers and corresponding weights, so as to accelerate clustering processing and reduce memory consumption. Extensive experiments are performed on UCI standard database, real cancer data set, and brain CT image data set. The experimental results show that the proposed method is superior to previous methods in less time consumption and good clustering performance.