Clustering method based on visual principle for solving big data clustering

Abstract

The invention discloses a clustering method based on a visual principle for solving big data clustering. The clustering method realizes multi-scale and multi-dimensional gridding storage of data by performing lossless multi-scale encoding on original data with given precision, judges similarity between codes and neighborhood codes based on codes of various scales, realize multi-scale clustering byutilizing connectivity analysis, and provides a multi-scale clustering result. The visual principle is utilized in the data coding process, the visual principle is consistent with a Weber's law, thatis, a difference threshold of sensory varies with the change of an original stimulus quantity.

Description

technical field [0001] The invention belongs to the field of big data clustering, and in particular relates to a visual principle-based clustering method for solving big data clustering. Background technique [0002] Clustering is a knowledge discovery method that divides data into different groups according to some similarity (such as structure or trend) of data. Measuring the similarity between data is the basis of clustering. Usually, the similarity between each point is stored in the form of a matrix. For large-scale or distributed data, this method will lead to a huge amount of data transmission, slow calculation efficiency, and even due to the huge matrix Unable to store the problem. [0003] The reason for these problems is that the similarity is stored in a dense matrix, and the data volume increases at the square speed of the original data volume. [0004] There are currently two types of big data clustering algorithms: [0005] A divisional clustering method wit...

AI Technical Summary

Problems solved by technology

This is difficult to meet in big data and distributed situations, so this method is also difficult to meet the needs of clustering

[0007] Clustering problems are the basis of information processing methods such as artificial intelligence and machine learning. There are many excellent clustering algorithms, but they are difficult to implement in the environment of big data computing, and the existing big data clustering methods are difficult to meet the needs of use.

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