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

Abstract

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.

VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation

“…Clustering approaches are unsupervised learning techniques that separate data into groups called clusters according to the similarities and dissimilarities among the data [ 1 , 2 ]. The DBSCAN [ 3 ], kmeans [ 4 ], BIRCH [ 5 ], Spectral Clustering [ 6 ], Agglomerative Clustering [ 7 ], HDBSCAN [ 8 ], Affinity Propagation [ 9 ], and OPTICS [ 10 ] are some examples of them, and they are used in many fields such as pattern recognition [ 11 – 13 ], machine learning [ 14 – 16 ], data mining [ 17 , 18 ], web mining [ 1 , 19 ], bioinformatics [ 20 , 21 ], and streaming data mining [ 22 , 23 ]. On the other hand, measuring the performance of any proposed clustering approach is also an important issue because each algorithm has its special point of view, and the results of each clustering technique vary.…”