Large-data clustering optimization method based on dimension reducing grouping

Abstract

The invention discloses a large-data clustering optimization method based on dimension reducing grouping. The large-data clustering optimization method includes the steps that (1) initialization is carried out; (2) similarity expressions corresponding to large-data clustering optimization problems are scanned, and whether relative symbols exist or not is judged; (3) relative dimensionality is stored; (4) whether similarity sub-expressions exist or not is judged; (5) ephemeral data of the similarity sub-expressions is stored; (6) whether relative symbols exist in the similarity sub-expressions or not is judged; (7) relative sub-dimensionality is stored; (8) whether a first symbol after the similarity sub-expressions is the similarity symbol or not is judged; (9) the relative dimensionality is merged; (10) ephemeral data is released; (11) sub-dimensionality with common elements is merged. By means of the large-data clustering optimization method based on dimension reducing grouping, the large-data clustering optimization problems can be accurately subjected to dimension reducing grouping, the speed is high, and wide adaptation is achieved.

Description

technical field [0001] The invention belongs to the technical field of big data clustering optimization, and further relates to a big data clustering optimization method based on dimensionality reduction grouping in the technical field of large-scale numerical query analysis and optimization. The present invention can be used for big data clustering, big data compression storage, classifier construction, optimizing scheduling process by grouping tasks in large-scale task scheduling, artificial neural network classification training, and classifying various system parameters in engineering design problems optimization etc. Background technique [0002] Big data clustering optimization technology refers to large-scale problems, that is, problems with more than 1,000 dimensions or variables, reducing the dimension of the problem through dimensionality reduction and grouping, so that it is easier to process or optimize large-scale problems. Due to the characteristics of high di...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to propose a large data clustering optimization method based on dimensionality reduction grouping for the existing large data clustering optimization problem solving methods with large amount of calculation and low accuracy, including the following specific steps:

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