Integrated multi-objective evolutionary automatic clustering method based on minimum spinning tree

Abstract

The invention provides an integrated multi-objective evolutionary automatic clustering method based on minimum spinning tree through which the problem of poor processing effectiveness to a high dimensional dataset in the existing technology is overcome. The realization steps of the method comprise: (1) inputing a genetic dataset of a cluster to be clustered; (2) initializing the genetic dataset; (3) setting an iteration parameter; (4) calculating the similarity of clusters; (5) generating a minimum spinning tree; (6) cutting off the minimum spinning tree; (7) combining the clusters; (8) rapidly arranging non-domination in order; (9) calculating the crawling degree; (10) generating a new parent population; (11) judging the iteration number to be smaller than 50 or not; (12) selecting an optimum individuality; (13) and calculating the accuracy value of the optimum individuality. According to the invention, the operation speed of the method is fast; clustering analysis to various genetic datasets can be carried out effectively without presetting number of categories of the datasets; and the method can be applied to the high dimensional data analysis existing in the fields like biomedical recognition and tumor detection.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for efficient searching by generating minimal paths through large amounts of data without producing any legal or harmful side-effects from existing methods like Cohen's algorithms. It also enhances the efficiency of finding specific parts within these data points based on their similarity between them.

Problems solved by technology

Technological Problem addressed in this patents relates to improving the efficiency and effectiveness of analyzing complex datasets like genomics while also considering factors like structure bias and spuriousness among these variables. Existing methods require manual input and may result in errors due to lack of knowledge about specific attributes associated with each dataset' characteristics. Therefore there needs an improved way to efficiently identify relevant relationships within big data sources without manually labelled categorization techniques.

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