Nerve network clustering method based on iteration

Abstract

The invention relates to a nerve network clustering method based on iteration. The method comprises steps that 1, parameters of an over-limit learning machine model are initiated; 2, samples having the same number as the required clustering number are randomly selected, each sample represents one clustering, an initial model sample set is formed, and an initial over-limit learning machine model is acquired through training; 3, clustering grouping for the samples is carried out by utilizing the initial over-limit learning machine model, and a clustering result is acquired; 4, for each clustering group, multiple samples are selected according to rules as models of the clustering groups; 5, the model samples of each clustering group acquired in the step 4 are utilized to update the over-limit learning machine model; and 6, the process returns to the step 3 for iteration till the clustering groups are stable or iteration frequency requirements are satisfied, and the clustering groups are acquired and outputted. The method solves a high-dimensional non-linear data space clustering problem and further solves problems of large memory consumption and long operation time.

Description

technical field [0001] The invention is mainly used to solve the classic clustering problem in machine learning, and the use method relates to the algorithm of extreme learning machine improved by artificial neural network. Background technique [0002] Data clustering has always been an important research content in industrial systems and computing science. In this era of big data, where the amount of information is exploding, business requirements related to data processing are increasing, and the data to be processed is becoming more and more complex. With people's in-depth research on clustering problems, new clustering methods are constantly being extracted. Traditional clustering algorithms such as K-means algorithm, maximum expectation algorithm, and hierarchical clustering algorithm have fast clustering speed but are only suitable for linearly separable data spaces. Emerging clustering algorithms, such as spectral clustering algorithm, deep learning autoencoder, un...

AI Technical Summary

Problems solved by technology

Emerging clustering algorithms, such as spectral clustering algorithm, deep learning autoencoder, unsupervised extreme learning machine, etc., can solve the situation where the data space is high-dimensional and linearly inseparable, but usually consume a large amount of memory or require a long operation hours

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