Particle swarm classifying method based on automatic clustering

Abstract

The invention discloses a particle swarm classifying method based on automatic clustering, which mainly solves the problems in the prior that the reference of domain information is limited, and the target function accessing standard is single. The method comprises the following processes: (1) carrying out an automatic clustering method on training data so as to obtain a class mark of the automatic clustering method; (2) carrying out the particle swarm optimal classifying method on the training data so as to obtain the class mark of the classifying method; (3) calculating the fitness value of the particle, and calculating the optimal relationship matrix; (4) replacing the positions of the particles; (5) updating the maximum historical fitness value and the maximum comprehensive historical fitness value of the particle; (6) determining whether the algorithm meets the terminating conditions, if so, stopping iterating, if not, carrying out step (3); (7) determining the class mark of the data based on the particle cluster; and (8) calculating the accuracy of classifying. The particle swarm classifying method based on automatic clustering has the advantages of obvious UCI (Uplink Control Information) data classifying effect, and can be applied to classifying the texture image.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to data classification, and can be used for texture image classification. Background technique [0002] With the increasing scale of the database, the amount of data accumulated by humans is growing exponentially. After entering the 1990s, with the emergence and development of the Internet, as well as the subsequent generation and application of enterprise intranets, enterprise extranets and virtual private networks, the whole world has become a smaller global village. What is displayed in front of us is no longer limited to the huge database of this department and the industry, but an endless ocean of information. At the same time, more data is being collected in the computer at an unprecedented speed. Therefore, from a large amount of incomplete, noisy, fuzzy, random data, it is possible to extract information that is hidden in it and that people do not know in advance. The e...

AI Technical Summary

Problems solved by technology

However, when using these two methods to update and iterate individuals, there are limitations to the participation of neighborhood information

Secondly, different objective function designs will have a great impact on the results of the algorithm

In classification, the traditional objective function only uses the classification accuracy of data as the evaluation standard. When using this type of function for discrimination, there is a lack of understanding of the characteristics of data distribution. These limitations and deficiencies limit its use in data classification. wide application of

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