Clustering method and system of parallelized self-organizing mapping neural network based on graphic processing unit

Abstract

The invention relates to a clustering method and system of a parallelized self-organizing mapping neural network based on a graphic processing unit. Compared with the traditional serialized clustering method, the invention can realize large-scale data clustering in a faster manner by parallelization of an algorithm and a parallel processing system of the graphic processing unit. The invention mainly relates to two aspects of contents: (1) firstly, designing the clustering method of the parallelized self-organizing mapping neural network according to the characteristic of high parallelized calculating capability of the graphic processing unit, wherein the method comprises the following steps of obtaining a word-frequency matrix by carrying out parallelized statistics on the word frequency of keywords in a document, calculating feature vectors of a text by parallelization to generate a feature matrix of data sets, and obtaining a cluster structure of massive data objects by the parallelized self-organizing mapping neural network; and (2) secondly, designing a parallelized text clustering system based on a CPU / GPU cooperation framework by utilizing the complementarity of the calculating capability between the graphic processing unit (GPU) and the central processing unit (CPU).

Description

technical field [0001] The invention relates to a parallel self-organizing map neural network clustering method and system, in particular to a graphic processing unit-based parallel self-organizing map neural network clustering method and system. Background technique [0002] At present, with the popularization of computers, the number of Internet users continues to increase, and Internet users generate a large amount of information on the Internet every day. At the same time, in some social media systems with a large number of users, a large amount of new data is added every day. Data mining and machine learning algorithms provide feasible methods for us to extract valuable information from these data, but most of the algorithms have complex learning processes, require iterative learning, and take a long time to process massive data. Although useful information is extracted, the information may no longer be time-sensitive, which requires the development of faster algorithm...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to construct a parallelized self-organizing mapping neural network clustering method and system based on a Graphic Processing Unit (Graphic Processing Unit, referred to as "GPU"), which overcomes the limitations of existing technologies in The technical problem of slow calculation speed due to the large amount of data in the process of text clustering

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