# AI News, Shenyang Normal University

- On 31. januar 2020
- By Read More

## Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

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.

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.

Through the support of existing technologies, relevant medical research organizations only rely on coupled dictionary technology to classify and store medical images [1].

However, with the continuous increase of the number of slices, some images begin to show serious frame rate overlap phenomenon, which not only causes the sharp decline of the original image gray level but also causes a series of image data redundancy problems.

The so-called image data redundancy refers to the phenomenon of uneven or excessive storage caused by data repetition in the process of data imaging that can lead to the real information loss in the image and cause a certain negative impact on the image sharpness.

But excessive frame rate overlap will lead to serious damage to the modal property of the medical image, which will lead to a large increase of the redundant region in the medical image data.

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].

Intelligent bionic algorithms mainly include ant colony algorithm [4], particle swarm optimization (PSO) algorithm [5], and the quantum swarm algorithm [6–8].

Domestic researchers mainly focus on the following two aspects: (1) a clustering algorithm dynamically determines the number of clustering centers and (2) a clustering algorithm improves the accuracy of clustering.

the main idea of the method was that, in order to effectively overcome the sensitivity to the initial state value clustering algorithm, it used the maximum attribute value range partitioning strategy and two stages and dynamic selection method in mutation, which obtained the optimal clustering center.

The task of cluster is to divide an unmarked pattern according to the certain criteria into several subsets, which requires that similar samples have the most similar cluster center and dissimilar samples should be divided in different classes.

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 [14–16].

[17] presented that the MapReduce programming model was adopted to combine Canopy and K-means clustering algorithms within cloud computing environment, so as to fully utilize the computing and storing capacity of Hadoop clustering.

We collected triple-negative breast neoplasm gene expression data from the Cancer Genome Atlas to construct a triple-negative breast neoplasm gene regulatory network using least absolute shrinkage and selection operator regression.

Hence, we propose a new medical big data clustering algorithm based on modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper.

In order to evaluate the data clustering and mining in the cloud computing environment, it needs to build a big data storage system architecture in cloud computing environment.

When all the cloud computing virtual machines are assigned to the physical machine, it uses the following formula to calculate the global optimal solution in this clustering process.

And, it also can assign big data feature clustering center of the cloud computing on the physical machine according to the optimal solution: The sample is collected and analyzed to determine whether the sample belongs to a typical sample.

In the process of multichannel QoS demand virtual machine clustering, some parameters are defined as virtual machine set and physical machine set .

The vector expression of big data clustering space in the cloud computing environment iswhere is information stream time series of big data clustering in cloud computing environment and is data sampling interval.

The spectral characteristic of discrete samples of big data can be calculated aswhere is the characteristic scalar time series of big data, is the discrete sample center of big data clustering, and is sample data high-order Bessel function statistics of data set .

The expression of clustering space state vector of big data in cloud computing environment is as follows:where is the information flow time series of big data clustering system in cloud computing environment, J is the time window function of phase space reconstructed by big data in cloud computing environment, M is the target clustering regulator, and is the data sampling interval.

The discrete sample spectral characteristic of big data is calculated, and the main feature component iswhere is the characteristic scalar time series of big data and is the center of discrete sample of big data clustering.

The immune evolutionary algorithm can improve the fitness of the individual and prevent the group degradation, so as to reduce the original wave phenomenon in the late evolutionary algorithm and improve the convergence speed.

The main steps for immune evolutionary algorithm are as follows, and the detailed information can be obtained from [29, 30].(1)Randomly generate the initial parent group .(2)Extract the vaccine according to prior knowledge.(3)If the current group contains the best individual, it stops running the process and outputs the result.

Otherwise, the procedure continues to work.(4)Cross operation of the current k-th group is conducted, and it obtains the population .(5)It makes mutation operation for and obtains the population .(6)It executes vaccination for and gets group .(7)It executes immune selection for and obtains new parent group .

The detailed improved data clustering processes are as follows: The objective function of FCM iswhere is the distance from k-th data point to i-th cluster center, denotes the cluster center of each class, and and are fuzzy index: According to , the aim of cluster is to obtain fuzzy division matrix U and cluster prototype V of sample X.

Set fuzzy index , stop condition , total population number , crossover probability , mutation probability , vaccination probability , and vaccine update probability . Step 2.

Compute fitness of every individual.(1)Each individual is decoded to calculate each prototype parameter .(2)Use and (8) to calculate .(3)Calculate . If , If , where and .(4)Use U, , and (7) to calculate object function , and then it can get for each individual. Step 4.

Then, it decodes the best individual, the clustering prototype is calculated, the classification results of each sample are calculated, and this classification result is the clustering result of data set X.

F-measure is calculated from two information indexes, precision, and recall rate, defined aswhere represents the class generated by the cluster method, denotes the class label of original dataset, and and represent recall rate and precision, respectively.

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