School of Computer and Information Engineering, Nantong Polytechnic College, Nantong 226001, Jiangsu, China.
Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi 214023, Jiangsu, China.
Comput Intell Neurosci. 2022 Feb 21;2022:6168785. doi: 10.1155/2022/6168785. eCollection 2022.
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.
随着人工智能的快速发展，各种医疗设备和可穿戴设备层出不穷，使得人们能够在医院或其他地方收集自己的各种健康数据。这导致医疗数据的规模大幅增加，不可能一次性将这些数据全部导入到内存中。因此，计算机的硬件要求变得更高，消耗的时间也增加了。本文介绍了一种在线聚类框架，将大数据集划分为若干个小数据块，通过加权聚类对每个数据块进行处理，得到每个数据块的聚类中心和相应的权重。最后，通过处理这些聚类中心和相应的权重，得到最终的聚类中心，从而加速聚类处理，减少内存消耗。在 UCI 标准数据库、真实癌症数据集和脑 CT 图像数据集上进行了广泛的实验。实验结果表明，该方法在消耗时间更少和聚类性能良好方面优于以前的方法。
