Data clustering analysis method based on genetic algorithm

Abstract

The invention particularly relates to a data clustering analysis method based on a genetic algorithm. The data clustering analysis method based on a genetic algorithm comprises the following steps: firstly, selecting an initial population from a sample set to be clustered; executing a genetic algorithm on the selected initial population; executing k-means operation on the new population generatedafter the genetic algorithm is executed; and repeating step (A) to step (C) until the optimal solution of the clustering problem is found. In the data clustering analysis method based on a genetic algorithm, local optimization of the K-means algorithm and global optimization of the genetic algorithm are combined, the optimal clustering number and the initial centroid set are finally obtained through multiple times of selection, intersection and variation genetic operation, the locality and sensitivity to the initial clustering center of a traditional K-means algorithm are overcome, and effective classification of data is achieved.

Description

technical field [0001] The invention relates to the technical field of genetic algorithms, in particular to a data clustering analysis method based on genetic algorithms. Background technique [0002] Clustering analysis is an unsupervised learning process, which refers to clustering things into clusters according to certain attributes of things, so that the similarity between clusters is as small as possible, and the similarity within clusters is as large as possible, so as to realize the classification of data. Cluster analysis is an important part of data mining technology. It can be used as an independent data mining tool to obtain the distribution of data in the database, and it can also be used as a preprocessing step for other data mining algorithms. Clustering analysis has become the main research field of data mining, and has been widely used in the fields of pattern recognition, image processing, data analysis and customer relationship management. [0003] K-means...

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Problems solved by technology

[0006] (2) How to construct a fitness function to measure the adaptability of each individual to the clustering problem, that is, if the code of an individual represents a good clustering result, its fitness is high; otherwise, its fitness is low

[0009] In view of the global optimization of the genetic algorithm, aiming at the shortcomings of the most widely used K-means method, the present invention proposes a K-means clustering algorithm based on the genetic algorithm to overcome the locality and the limitation of the initial K-means algorithm. Sensitivity of cluster centers

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