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A K-means clustering method based on improved moth fire fighting

A moth-to-fire, clustering method technology, applied in the field of swarm intelligence, can solve the problems of sensitive initial cluster center selection, low clustering efficiency and accuracy, and poor global search ability, achieving strong local search ability and improving efficiency. , clustering fast effect

Inactive Publication Date: 2019-04-09
CHANGAN UNIV
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AI Technical Summary

Problems solved by technology

K-Means Clustering (K-Means Clustering, KMC) method is a clustering method based on the partition idea. It has the advantages of simple thinking, fast clustering, and strong local search ability, but it is also sensitive to the selection of the initial clustering center. , poor global search ability, clustering efficiency and low accuracy limitations

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  • A K-means clustering method based on improved moth fire fighting
  • A K-means clustering method based on improved moth fire fighting
  • A K-means clustering method based on improved moth fire fighting

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Embodiment Construction

[0044] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0045]The initial value of the clustering center of the existing K-means method is randomly selected, and the result is related to the initial clustering center, so it is very easy to fall into a local optimal solution, which affects the accuracy of the final result. The convergence speed of the fire algorithm is relatively slow, and the solution accuracy is insufficient. In view of the above problems, the present invention provides a K-means clustering method based on improved moths catching fire, see figure 1 , it not only takes advantage of the K-means method’s advantages of simple thinking, fast clustering, and strong local search ability, but also uses the characteristics of the moth-flame algorithm that can use each flame position to update the moth’s position to avoid falling into a local optimum Advantages of the solution to achieve a good clustering effect. ...

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Abstract

The invention discloses a K-means clustering method based on improved moth fire fighting, and the method comprises the steps: firstly inputting a standard data set, i.e., a moth group, and determiningthe number of classes in the data set according to the number of classes of the data set; Secondly, determining initial moths by using a maximum and minimum distance product method, calculating distances between other moths except the initial moths and the initial moths, and performing clustering division according to the minimum distance; Then, obtaining a new clustering center for each class byusing a moth fire extinguishing algorithm, and finally, continuously and alternately updating clustering center points by using the moth fire extinguishing algorithm and a K-means method until specified iteration times are reached, and the finally obtained clustering center points are final clustering center points.

Description

technical field [0001] The invention belongs to the field of swarm intelligence methods, and in particular relates to a K-means clustering method based on improved moths fighting flames. Background technique [0002] With people's continuous understanding of the nature of life, life science is developing at an unprecedented speed, making the research of artificial intelligence begin to get rid of the shackles of classical logic computing, and boldly explore new non-classical computing methods. In this context, the self-organization behavior of social animals (such as ant colony, bee colony, bird flock, etc.) has attracted extensive attention. The beauty of social animals is that individual behaviors are simple, but when working together, they can highlight the characteristics of intelligent behavior. Mathematically modeling this behavior and simulating it with a computer is the swarm intelligence approach. [0003] The swarm intelligence method has become the focus of more...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/23213
Inventor 黄鹤郭璐徐锦许哲茹锋黄莺惠晓滨何永超任思奇李光泽胡凯益
Owner CHANGAN UNIV
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