Non-supervision clustering method of complicated system

Abstract

The invention discloses an unsupervised pile collection method in a complex system, which comprises the steps that: discrete characteristic variables and class variables are determined according to the original information of a complex system sample; the relevancy between two characteristic variables is calculated; 'the crowd of friends and relatives' of each characteristic variable is determined; the unsupervised pile collection is carried out to the characteristic variables according to the self-organization of the pile collection to obtain the combination of the characteristic variables; each pile is back substituted into original data to obtain the sensitivity; the degree of the sensitivity is judged; verification is carried out to the unsupervised pile collection method by utilizing the class variables of the system to obtain the optimum combination of the characteristic variables. The method solves the problem that the traditional relevancy can not distinguish positive correlation and negative correlation, has self-organization, needs no human intervention, has high running speed, and is suitable for a large amount of data, even mass data. Furthermore, the method can realize clustering and the appearance of certain variables in certain different classes, can carry out the verification to the unsupervised pile collection so as to find out the optimal pile, and has wide application value in the fields such as ecological differentiation and clinical medical data analysis, etc.

Description

Technical field [0001] The invention belongs to the field of complex system science and data mining, and relates to a complex system entropy accumulation method based on improved correlation coefficient. Background technique [0002] Entropy partitioning of complex systems is currently the only unsupervised clustering method for complex systems. This method is based on the traditional correlation coefficient method to draw the information connection graph, and then artificially classifies, each class corresponds to a subsystem. However, this method has two disadvantages: [0003] (1) It is not self-organizing, it needs to be determined artificially, and to achieve "rigid" classification, it cannot realize that certain characteristic variables appear in different subsystems. [0004] (2) The lack of verification in this method makes it impossible to give optimal results for many of the results obtained. Summary of the invention: [0005] The traditional technical relevance to be ...

AI Technical Summary

Problems solved by technology

[0005] The traditional technical correlation degree that the present invention intends to solve cannot distinguish the technical problem of positive correlation and negative correlation. For this reason, the present invention proposes a fast, self-organizing method that can not only realize clustering, but also realize certain variables in different subsystems. Unsupervised Clustering Methods in Complex Systems

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