Clustering-decision tree based selection method of fine corn seeds

Abstract

The invention discloses a clustering-decision tree based selection method of fine corn seeds, comprising the following steps: preprocessing data; establishing a decision tree; systematically judging the class of an input attribute value according to the established decision tree, and obtaining a point with a minimum distance from a three-dimensional point by computing the distances between the three-dimensional point and other points in the class, wherein the attribute of the point is most approximated to the corn attribute of an input corn seed; and querying the parent class and the mother class of the input corn seed from a children table so as to complete a corn seed selection function. The invention can optimize the fine corn seeds in the generational information, the growth information, the harvest information, and the like of corns according to requirements by combining a clustering and decision tree algorithm and can achieve the purposes of reducing the labor intensity and enhancing the decision efficiency.

Description

Technical field: [0001] The invention relates to a method for breeding improved corn varieties. Background technique: [0002] Clustering is a common data analysis tool, which refers to the study and processing of a given object by mathematical methods, and is a type of multivariate statistical analysis. Based on the idea of ​​"like flocking together", it divides a large number of data points into several classes or clusters, so that the data in each class are similar to each other to the greatest extent, and the data in different classes are most different, so as to discover the global distribution. Schemas and the interrelationships between data attributes. A prominent feature of cluster analysis is the processing of huge and complex data sets, and it can also be used as a preprocessing step for other algorithms. Currently commonly used clustering algorithms include k-average, k-module, k-centroid, DIANA, AGNES, STING, COBWEB, etc. [0003] These classic clustering algo...

AI Technical Summary

Problems solved by technology

[0003] These classic clustering algorithms can only deal with simple and general data, and are not effective for large and complex data sets, so many improved and new algorithms are constantly being proposed.

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