Brain network clustering method based on local attributes and topological structure

Abstract

The invention discloses a brain network clustering method based on local attribute and topological structure. The method is carried out according to the following steps: firstly, the brain function network is pretreated and the average time series of each brain region is extracted; then the Pearson correlation coefficients are calculated to construct the unbiased brain function network. The similarity of brain function network is calculated. At last, the similarity of brain functional network is clustered and the clustering results are tested. The invention obtains a higher precision similarity by weighted fusion of local attribute similarity degree and topological structure similarity degree of the brain function network. After clustering the similarity degree based on the local attributeand topological structure, the clustering result obtained is accurate and without bias.

Description

technical field [0001] The invention belongs to the technical field of machine learning, in particular to a brain network clustering method based on local attributes and topological structures. Background technique [0002] At present, machine learning, as an important tool for brain network analysis, has become a new research hotspot in the field of brain network analysis in recent years because it can learn rules from data and predict unknown data. Supervised learning (classification) and unsupervised learning (clustering). [0003] Most of the current research uses supervised learning, that is, using labeled training data to train the classification model, and then using the classification model to classify the test data; however, since the labeling of the data is performed by professionals based on some prior knowledge Labeling is subjective, and errors may occur during the labeling process, which will eventually affect the classification results and reduce the accuracy...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a brain network clustering method based on local attributes and topological structures, to overcome the problem of inaccurate labeling in supervised learning, and to make the brain network similarity clustering results more accurate

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