Abstract
Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.
Highlights
Modified Immune Evolutionary Algorithm for Data ClusteringFuzzy clustering is regarded as one of the commonly used approaches for data analysis. e Fuzzy C-means (FCM) algorithm is the most well-known and widely used method for fuzzy clustering and provides an optimal way to construct fuzzy information granules [31]
With the fast growth of information science, the research of biological applications has been used for computational science to analyze the intelligent bionic optimization algorithm design and improve the ability of processing big data and analysis [3]
According to the clustering criterion, different clustering algorithms can be divided into clustering algorithm based on fuzzy relations including hierarchical clustering and graph clustering and clustering algorithm based on the objective function [14,15,16]
Summary
Fuzzy clustering is regarded as one of the commonly used approaches for data analysis. e Fuzzy C-means (FCM) algorithm is the most well-known and widely used method for fuzzy clustering and provides an optimal way to construct fuzzy information granules [31]. E former p quantized values denote the first p dimension cluster center It does not change with the data sample n. We can adopt a reverse genetic mutation operator, namely, it randomly generates a gene in the parent group and the gene is reverted. E other is an adaptive method, namely, in the process of group evolution from the best individual genes. It extracts useful information and executes the vaccine. Erefore, in the clustering algorithm based on immune evolution, we adopt the adaptive method to extract the vaccine. (4) Use U, Dik, and (7) to calculate object function J(X; U, V), and it can get f for each individual. Step 8. en, it decodes the best individual, the clustering prototype vi is calculated, the classification results of each sample are calculated, and this classification result is the clustering result of data set X
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